Generative AI & Agentic AI Engineer

PRABHU K. PRADHAN

Generative AI & Agentic AI Engineer RAG & Multi-Agent Systems Python, Java & Cloud

913-***-**** My LinkedIn Open Nationwide

Summary of Qualifications:

oGenerative AI and Agentic AI Engineer with 16+ years of enterprise engineering experience and hands-on delivery of FIS’s enterprise AI platform, now in business-user pilot, which has reduced manual effort and improved processing time and accuracy over earlier pilot phases across regulated financial workflows.

oHands-on expertise building Agentic and multi-agent AI systems with LangGraph, LangChain, and Model Context Protocol (MCP), including a four-agent solution that unifies inputs from up to 15 business applications into a single RAG pipeline for faster query retrieval, with stateful workflows, tool/function calling, and human-in-the-loop controls.

oExtensive experience designing Retrieval-Augmented Generation architectures covering document ingestion, chunking, embeddings, metadata enrichment, hybrid and vector search (pgvector, Chroma DB, Azure AI Search), reranking, and source attribution, and building LLM solutions with OpenAI APIs, Azure OpenAI, GPT-4o, Ollama-hosted LLaMA, Hugging Face models, AWS Bedrock, Amazon SageMaker, and Whisper using prompt orchestration, structured outputs, and model routing.

oStrong expertise in Responsible AI and LLM quality for regulated environments, including LLM evaluation (LLM-as-Judge, LangSmith) and observability, PII protection, prompt-injection defense, guardrails, RBAC, hallucination mitigation, and auditability.

oSkilled full-stack and AI platform engineer with Python (FastAPI, Flask, Django), Java, Spring Boot, Kafka, REST APIs, WebSockets, Angular, and microservices, deploying on Microsoft Azure, AWS, Docker, Kubernetes, and CI/CD pipelines (Azure DevOps, Jenkins).

oDeep financial-services domain experience across core banking, client onboarding, payments, lending, compliance, risk management, and digital-signature workflows at FIS, backed by earlier enterprise delivery for telecommunications, banking, and automotive clients (AT&T, Barclays, Volvo Cars).

oProven technical leader who has led a team of 7 engineers (4 developers, 2 QA, 1 BSA), mentored developers, run architecture reviews, and partnered with Product, Security, Risk, Audit, Compliance, and Enterprise Governance stakeholders.

Awards, Certification, Education:

oStar Performer Award, FIS Bangalore, for successfully migrating the Enterprise Customer project to automation.

oExcellence Award, Zensar Technologies, for successful cloud implementation.

oSAFe Certified Scrum Master, earned during FIS projects.

oBachelor’s Degree in Computer Science & Engineering (2009 Graduate) Biju Patnaik University of Technology - Odisha, India

Professional Experience:

Client: Fidelity Information Services (FIS) – Milwaukee, WI Mar 2025 – Present

Role: Generative AI Engineer, Sr. Java Full Stack Developer

Config-As-Code, FIS’s enterprise Generative AI platform for intelligent financial-document processing, conversational knowledge discovery, AI-assisted client onboarding, digital-signature automation, and compliance-aware decision support, combining Agentic AI, RAG, LLM orchestration, and secure enterprise integration across regulated financial workflows. Currently in pilot with business users. Co-designed the platform with the AI Solution Architect and led a team of 7 engineers (4 developers, 2 QA, 1 BSA) in two-week Agile sprints.

oDelivered pilot results that reduced manual effort and improved processing time and accuracy compared to earlier pilot phases, as AI adoption continues to expand.

oRolled out the platform as a pilot supporting 10 to 15 business workflows (applications), actively used by approximately 100 business users and product owners, with the underlying applications serving approximately 3,000 end-users. The next rollout phase is targeted for March 2027, expanding to 50% of end-users.

Agentic AI and multi-agent systems

oArchitected multi-step, stateful Agentic AI workflows using LangGraph and LangChain, orchestrating OpenAI APIs, Ollama-hosted LLaMA 3.1, Whisper, and enterprise tools to plan and route tasks, maintain conversational state, select tools, retrieve financial information, process documents, and coordinate downstream business actions across the pilot workflows.

oDeveloped a multi-agent system using LangGraph and MCP, built on a supervisor hub-and-spoke pattern with four agents (Router, Resolution, Communication, and Critic), that ingests inputs from up to 15 business applications into a single combined RAG pipeline, enabling business users and product owners to query consolidated information with faster retrieval.

oBuilt a multi-agent Card Delivery system proof of concept in which collaborating AI agents coordinate card-delivery workflow tasks: a central Routing agent for orchestration, a Resolution agent for front-end to REST to back-end workflows, a Communication agent for customer notifications and alerts, and a Critic agent for resolution evaluation, supported by tools for card-delivery logs, customer-profile lookup, availability checks, playbook search, and an escalation rule engine.

oDeveloped reusable MCP-based integration services and structured tool/function-calling patterns that standardize how agents discover and invoke approved enterprise APIs, applications, data sources, and document repositories and return controlled, machine-readable responses, using the OpenAI Agents SDK.

oEngineered prompt-orchestration and context-management frameworks using reusable templates, few-shot prompting, role-based instructions, structured outputs, and dynamic context assembly that preserve conversational history, document context, user entitlements, and workflow state across multi-turn interactions, improving response consistency and reliability.

oImplemented model-routing patterns across OpenAI and locally hosted LLaMA models based on privacy, performance, availability, and use-case requirements, and used AWS Bedrock and Amazon SageMaker for managed model access.

oIntegrated Whisper-based speech recognition into conversational workflows, enabling secure voice-to-text and voice-assisted document interactions.

RAG and knowledge retrieval

oCo-designed the end-to-end RAG architecture, including document ingestion, content extraction, chunking, embeddings, metadata enrichment, semantic search, reranking, and pgvector retrieval, grounding LLM responses in approved enterprise content for contextual Q&A, document comparison, compliance validation, and knowledge discovery. Used hybrid keyword and vector search, Chroma DB vector stores, Hugging Face embedding models such as BAAI, and LangChain’s RecursiveCharacterTextSplitter chunking strategy.

Evaluation, governance, and reliability

oDesigned LLM evaluation practices using LLM-as-Judge and LangSmith to measure retrieval accuracy, contextual recall and precision, tool-call accuracy, response quality, latency, and failure patterns across RAG and Agentic AI workflows, with measurable KPI improvements across pilot iterations.

oEstablished AI governance and guardrails covering PII protection, prompt-injection defense, input sanitization, output validation, access control, response traceability, and auditability, with human-in-the-loop approval checkpoints for sensitive or high-impact workflows.

oApplied hallucination-mitigation techniques through source-grounded generation, retrieval validation, prompt constraints, structured responses, fallback handling, and citation of supporting content.

oEstablished LLM observability and traceability across prompts, retrieved context, model responses, tool executions, token usage, and workflow transitions to support debugging, governance, and operational monitoring using LangSmith.

oOptimized AI response performance through asynchronous processing, contextual caching, parallel service execution, connection pooling, and streaming.

Platform engineering and integration

oEngineered secure AI platform services using Python, FastAPI, Flask, Django, Java, and Spring Boot for model inference, document processing, retrieval, and agent orchestration, delivering real-time conversational experiences through REST APIs and WebSockets in Angular 17 applications.

oImplemented Kafka-based event-driven processing and integrated enterprise systems (client onboarding, account verification, document generation, compliance validation) to coordinate AI workflows and asynchronous tool execution.

oSecured AI services and enterprise APIs using Spring Security, JWT, and RBAC so that model responses, retrieved content, and available tools respected user-level permissions.

oBuilt AI-enabled DocuSign workflows for intelligent document classification, data extraction, automated prefilling, multi-party approvals, secure signing, and audit tracking.

oContainerized LLM, retrieval, Python, and Java services with Docker and GPU-enabled inference for locally hosted models, and automated build, test, and deployment with Azure DevOps and Jenkins across model-integration, backend, and UI services.

oImplemented automated testing with JUnit and Mockito and centralized logging, exception handling, monitoring, and distributed traceability to support platform reliability and regulatory audit readiness; leveraged GitHub Copilot, Cursor AI, Claude Code, and Windsurf for code analysis, refactoring, debugging, and test generation, improving team productivity.

Leadership and collaboration

oLed and mentored a team of 7 engineers (4 developers, 2 QA, 1 BSA), owning sprint planning and code and architecture reviews, and collaborated with Product Owners, Solution Architects, AI Engineers, security, and compliance stakeholders to define the AI architecture and accelerate secure adoption of Generative AI across FIS.

Environment: Generative AI, Agentic AI, Multi-Agent Systems, Large Language Models, Retrieval-Augmented Generation, LLM Orchestration, LLMOps, Prompt Engineering, Structured Outputs, Tool/Function Calling, LangChain, LangGraph, Model Context Protocol, OpenAI APIs, Ollama, LLaMA 3.1, Whisper, AWS Bedrock, Amazon SageMaker, NLP, Embeddings, Document Chunking, Semantic Search, Vector Search, Metadata Filtering, Reranking, pgvector, Context Management, Model Routing, AI Guardrails, Prompt-Injection Protection, PII Protection, Hallucination Mitigation, Human-in-the-Loop, LLM Evaluation, LLM-as-Judge, LangSmith, LLM Observability, Conversational AI, Python, FastAPI, Flask, Django, Java, Spring Boot, Spring Security, JWT, RBAC, REST APIs, Swagger/OpenAPI, Kafka, Microservices, Event-Driven Architecture, WebSockets, Angular 17, TypeScript, RxJS, DB2, Docker, GPU-Enabled Inference, Azure DevOps, Jenkins, Git, JUnit, Mockito, DocuSign APIs, GitHub Copilot, Cursor AI, Claude Code, Windsurf, CI/CD, Audit Trails, Secure Document Processing, Real-Time Workflow Automation, Supervisor Orchestration, Hybrid Search, Chroma DB, Hugging Face, OpenAI Agents SDK

Client: Fidelity Information Services (FIS) – Bangalore, India May 2017 – Feb 2025

Role: Generative AI Engineer Sr. Enterprise Java Full Stack Developer

Long-term engagement supporting FIS’s Enterprise Customer core banking platform for 8 banks and financial institutions globally across retail banking, lending, payments, compliance, and risk management. Led enterprise Java full-stack development, technical modernization, and cloud-native delivery for most of the engagement, then moved into FIS’s Generative AI initiative in its later phase, integrating secure AI capabilities into the existing banking ecosystem. Team of 7 engineers in two-week Agile sprints.

Generative AI Initiatives (2023–2025)

oContributed to the architecture and implementation of a secure enterprise Generative AI platform in a ring-fenced Microsoft Azure environment, enabling data isolation, controlled model access, governance, and compliance-driven AI adoption across applications and business units.

oIntegrated Azure OpenAI, GPT-4o, LangChain, and enterprise LLM services into banking applications for intelligent knowledge discovery, financial-document interpretation, contextual Q&A, and content summarization, supporting multiple use cases.

oDesigned RAG pipelines with LangChain, Azure OpenAI, and Azure AI Search, enabling semantic and vector-based retrieval across approved enterprise knowledge repositories holding thousands of documents.

oEngineered document-ingestion pipelines for PDFs, structured datasets, APIs, and unstructured content, covering content extraction, chunking, embedding generation, metadata enrichment, and semantic indexing.

oDeveloped Python and FastAPI services exposing AI inference, document processing, and retrieval through secure REST APIs for downstream banking applications.

oSupported multi-model orchestration across GPT-4o, GPT-4o-mini, and locally hosted models through LM Studio, using GPT-4o for LLM-as-Judge evaluation and heavy computation and GPT-4o-mini for retrieval and lighter tasks, balancing response quality, privacy, performance, and cost efficiency.

oDeveloped a secure LLM gateway pattern that routes model requests through protected enterprise backend services, preventing direct application exposure to external AI endpoints and enforcing centralized access policies.

oApplied prompt-engineering techniques (contextual grounding, reusable templates, few-shot prompting, response constraints, and structured outputs) to improve the accuracy and consistency of AI responses.

oImplemented Responsible AI and governance controls covering PII protection, input validation, response guardrails, hallucination mitigation, policy enforcement, and sensitive-data handling, with source attribution, response-lineage tracking, and audit traceability for explainability and compliance.

oImplemented RBAC and JWT-based access controls and established monitoring and audit logging across AI requests, retrieved context, responses, and exceptions to support operational oversight and compliance reporting.

oOptimized platform performance through asynchronous processing, caching, and efficient vector queries, and containerized AI services with Docker and Kubernetes-based deployment patterns.

oCollaborated with Risk, Audit, Compliance, Enterprise Governance, Product, and Architecture teams to translate regulated requirements into secure Generative AI solutions, and mentored 7 engineers on Generative AI integration, RAG, prompt engineering, and LLM security.

Enterprise Java Full Stack Development and Technical Leadership (2017–2023)

oDesigned and developed enterprise core banking applications using Java, Spring Boot, REST APIs, and Microservices Architecture for client onboarding, payments, lending, compliance, risk management, and workflow automation.

oBuilt scalable backend services and reusable business components using modular architecture and enterprise design patterns, improving maintainability, performance, and reliability.

oDeveloped secure application services with Spring Security, JWT authentication, and role-based access control to protect customer data and regulated financial transactions.

oDesigned responsive frontend applications using Angular, React, TypeScript, and GraphQL for complex banking workflows, built on a reusable Micro-Frontend Architecture shared across applications.

oServed as Technical Lead for frontend engineering, leading a team of 7 engineers (5 developers and 2 QA engineers) and coordinating delivery with Product Owners, Business Analysts, Solution Architects, and distributed teams.

oDesigned and implemented integrations for customer onboarding, account servicing, payments, document processing, and compliance validation, using asynchronous and distributed processing to improve scalability and responsiveness across high-volume banking workflows.

oDesigned and maintained CI/CD pipelines with Azure DevOps and Jenkins, automating builds, testing, code-quality validation, security scanning, and controlled deployments.

oContainerized enterprise applications with Docker and supported deployments across AWS ECS, Kubernetes, and OpenShift environments.

oIntegrated Checkmarx and CxOne into the development lifecycle to find application-security vulnerabilities and strengthen secure-coding and release-governance practices.

oEstablished automated unit and integration testing, raising code coverage and improving regression testing and release confidence.

oLed performance optimization, production troubleshooting, centralized logging, and monitoring for system stability, and owned technical design decisions, delivery planning, architecture reviews, and coding standards across modernization initiatives, mentoring developers on Java, Spring Boot, microservices, frontend architecture, and secure development.

Environment: Generative AI, Large Language Models, Retrieval-Augmented Generation, LLM Orchestration, Azure OpenAI, GPT-4o, LangChain, Prompt Engineering, Semantic Search, Vector Search, Embeddings, Document Chunking, Azure AI Search, Responsible AI, AI Guardrails, Hallucination Mitigation, Explainable AI, Source Attribution, Secure LLM Gateway, LM Studio, GitHub Copilot, Cursor AI, Python, FastAPI, Java, Spring Boot, REST APIs, Microservices Architecture, Distributed Systems, Asynchronous Processing, Angular, React, TypeScript, GraphQL, Micro-Frontend Architecture, Spring Security, JWT Authentication, RBAC, Application Security, Microsoft Azure, AWS ECS, Docker, Kubernetes, OpenShift, Azure DevOps, Jenkins, CI/CD, Git, Checkmarx, CxOne, Automated Testing, Performance Engineering, Monitoring, Observability, Audit Logging

Client: Cisco – USA

Employer: Zensar Technologies – Pune, India Jan 2016 – Apr 2017

Role: Senior Software Engineer

Cisco enterprise application supporting business operations and cloud modernization, built on scalable service-oriented and microservices architectures. Delivered secure Java backend services, responsive web applications, cloud-native deployments, and DevOps automation as part of an Agile team.

oDesigned and developed scalable enterprise applications using Java, Spring Boot, and Microservices Architecture for distributed business workflows.

oBuilt secure RESTful APIs and backend services using Spring Boot, Spring Data JPA, and Hibernate, improving scalability, maintainability, and performance, with APIs documented through Swagger/OpenAPI.

oDeveloped responsive front-end applications using Angular 2+, TypeScript, HTML5, and CSS3 with reusable UI components, improving user experience and reducing front-end development effort.

oOptimized backend performance through database tuning, ORM optimization, connection pooling, and efficient query execution with Hibernate and Spring Data JPA on PostgreSQL, improving response times.

oDesigned AWS cloud-native solutions using EC2, S3, RDS, and CloudWatch for secure, scalable, highly available deployments.

oContainerized enterprise applications with Docker, improving deployment consistency, portability, and scalability.

oImplemented CI/CD pipelines and DevOps automation with Jenkins to streamline builds, testing, environment provisioning, deployments, and release management.

oImplemented Feature Flag Architecture using Unleash to support canary deployments, controlled feature rollouts, and configuration management, lowering release risk.

oDeveloped automated unit and integration tests with JUnit, Mockito, and Selenium to improve software quality and release stability, and monitored production environments with AWS CloudWatch, application logging, and performance-monitoring tools to speed up incident resolution.

oParticipated in architecture discussions, code reviews, and Agile ceremonies with Product Owners, Solution Architects, QA Engineers, and cross-functional teams, and mentored junior developers on SOLID principles, design patterns, clean coding, and engineering best practices.

Environment: Java, Spring Boot, Spring Data JPA, Hibernate, REST APIs, Microservices Architecture, Angular, TypeScript, HTML5, CSS3, AWS (EC2, S3, RDS, CloudWatch), Docker, Jenkins, CI/CD Pipelines, DevOps Automation, PostgreSQL, JUnit, Mockito, Selenium, Git, Maven, Unleash, Swagger/OpenAPI, Agile/Scrum, SOLID Principles, Code Reviews, Release Management

Client: Derivco – South Africa

Employer: Zensar Technologies – Pune, India Dec 2014 – Dec 2015

Role: Java Full Stack Developer

Derivco’s web-based online gaming platform, modernized with scalable backend services, interactive user interfaces, cloud deployments, and new features to improve player engagement. Worked across development, QA, graphics, and animation teams in Agile sprints.

oDesigned, developed, and maintained enterprise gaming applications using Node.js, ExpressJS, AngularJS, JavaScript, and MongoDB, delivering gaming modules and features to players.

oAnalyzed business requirements and built scalable server-side modules and RESTful APIs for gaming workflows and backend business logic.

oBuilt dynamic, responsive user interfaces with AngularJS, JavaScript, HTML5, CSS3, ES6, and RxJS, improving page load time.

oMigrated legacy gaming modules from Adobe Flash to HTML5, improving browser compatibility, performance, and long-term platform sustainability, and enabling mobile-device support.

oDesigned and implemented an In-Game Achievement module with configurable rewards and milestone-based features to boost player engagement.

oDeveloped reusable ExpressJS middleware with Passport, Helmet, Body-Parser, and Cookie-Parser for authentication, application security, and request handling across services.

oUsed Mongoose and MongoDB for data access and built reporting modules with Carbone, EJS, JADE, and LibreOffice to generate business reports.

oManaged dependencies with NPM and Sinopia (private registry) and supported AWS application deployments and environment migrations across Ubuntu and Windows environments.

oCollaborated with development, QA, graphics, and animation teams to deliver gaming features with quality and usability, prepared HLD and FRD documentation, led client demonstrations, and contributed to sprint planning, code reviews, and defect resolution.

Environment: Node.js, ExpressJS, RESTful APIs, JavaScript, AngularJS, HTML5, CSS3, ES6, RxJS, MongoDB, Mongoose, AWS, NPM, Sinopia, EJS, JADE, Carbone, LibreOffice, Passport, Helmet, Body-Parser, Cookie-Parser, Ubuntu, Windows, Agile, Code Reviews, HLD, FRD, Client Demonstrations

Clients: AT&T – US, Barclays Bank – UK, Volvo Cars – Sweden

Employer: Tech Mahindra – Pune, India Mar 2012 – Nov 2014

Role: Java Full Stack Engineer

Supported several enterprise clients across Telecommunications (AT&T), Banking and Financial Services (Barclays), and Automotive (Volvo Cars), contributing to the design, development, enhancement, deployment, and production support of applications for customer onboarding, workflow automation, business process management, system integrations, and high-volume transaction processing.

oDesigned, developed, and enhanced enterprise applications using Java, J2EE, and the Spring Framework, delivering scalable business solutions across multiple client engagements.

oDeveloped RESTful services, enterprise integration modules, and backend components using Java, Servlets, JSP, and SQL for customer onboarding, workflow automation, and business transaction processing.

oDeveloped and enhanced enterprise workflow solutions using IBM BPM for business process automation, case management, regulatory workflows, and content management, reducing manual processing.

oUsed IBM CMOD, ODWEK, and SSH for enterprise content management, secure file transfers, document archival, and backend integrations.

oPerformed application deployments on IBM WebSphere Application Server (WAS), implemented production fixes, and resolved defects, improving reliability and reducing production incidents.

oConducted unit testing and code reviews, and contributed to performance optimization and continuous improvements.

oParticipated across the full SDLC (requirements analysis, development, testing, deployment, production support, and application maintenance) within Agile teams, delivering releases on schedule across global client engagements.

oCollaborated with business analysts, architects, QA engineers, and client stakeholders to prepare HLDs and technical documentation and to deliver scalable enterprise solutions.

Environment: Java, J2EE, Spring Framework, Servlets, JSP, RESTful Services, IBM BPM, Enterprise Workflow Automation, System Integration, IBM WebSphere Application Server (WAS), SQL, IBM CMOD, ODWEK, SSH, SDLC, Agile, Unit Testing, Code Reviews, HLD, Technical Documentation, Production Support

Client: Mahindra Satyam – Hyderabad, India Mar 2010 – Feb 2012

Role: Software Engineer

Enterprise business application supporting day-to-day business functions. Contributed to design, development, testing, and maintenance as part of a software engineering team, building a strong foundation in Java, enterprise software engineering, and the Software Development Life Cycle (SDLC).

oDeveloped enterprise application features using Java, J2EE, and the Spring Framework to meet business requirements.

oDeveloped backend business logic, SQL queries, and database enhancements on MySQL while supporting application integrations and improving query performance.

oPrepared and executed unit test cases, resolved defects, and performed debugging to improve software quality and production stability.

oSupported application deployments, release validation, and production support, delivering releases on schedule.

oCollaborated with developers, QA engineers, and business analysts in code reviews, technical discussions, and Agile practices, contributing to sprint and release delivery.

Environment: Java, J2EE, Spring Framework, JSP, Servlets, SQL, MySQL, Maven, Eclipse, Spring Tool Suite (STS), Unit Testing, SDLC, Agile/Scrum, Code Reviews, Production Support

Technical Skills:

Generative & Agentic AI: Generative AI, Agentic AI, LLMs, RAG, Multi-Agent Systems, Supervisor Orchestration, Stateful AI Workflows, LLM Orchestration, Tool/Function Calling, Structured Outputs, Model Routing, Human-in-the-Loop, Conversational AI

AI Frameworks & Platforms: LangChain, LangGraph, MCP, OpenAI APIs, OpenAI Agents SDK, Azure OpenAI, GPT-4o, AWS Bedrock, Amazon SageMaker, Ollama, LLaMA 3.1, LM Studio, Hugging Face, Whisper

RAG & Prompt Engineering: Prompt Engineering, Few-Shot Prompting, Document Ingestion, Chunking, Embeddings, Semantic, Hybrid & Vector Search, Reranking, pgvector, Chroma DB, Azure AI Search, Source Attribution

AI Governance & LLMOps: Responsible AI, AI Guardrails, Prompt-Injection Protection, PII Protection, Hallucination Mitigation, Secure LLM Gateway, Explainable AI, LLM Evaluation (LLM-as-Judge, LangSmith), LLM Observability, Audit Logging

Programming & Backend: Python, Java, JavaScript, TypeScript, SQL, FastAPI, Flask, Django, Spring Boot, Spring Data JPA, Hibernate, J2EE, Node.js, ExpressJS, REST APIs, Microservices, SOA, Distributed Systems

Frontend Technologies: Angular 17, Angular 2+, AngularJS, React, RxJS, HTML5, CSS3, Micro-Frontend Architecture

Messaging & Integration: Apache Kafka, Event-Driven Architecture, GraphQL, WebSockets, DocuSign APIs, IBM BPM

Cloud & DevOps: Microsoft Azure, AWS (EC2, S3, RDS, CloudWatch, ECS), Docker, Kubernetes, OpenShift, Azure DevOps, Jenkins, CI/CD, IBM WebSphere (WAS)

Databases: PostgreSQL, MySQL, DB2, MongoDB

Security & Compliance: Spring Security, JWT, RBAC, Checkmarx, CxOne, Regulatory Compliance, Auditability

Testing & Dev Tools: JUnit, Mockito, Selenium, Unit and Integration Testing, GitHub Copilot, Cursor AI, Claude Code, Windsurf, Git, GitHub, Maven, Swagger/OpenAPI, Postman, JIRA, Unleash, Eclipse, STS

Engineering Practices: SOLID Principles, Clean Architecture, Performance Optimization, Code Reviews, Agile/Scrum, Technical Leadership, Enterprise Modernization

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