Python Backend & AI Systems Engineer
Resume:
Nikhil Raj
Python Engineer AI & Backend Systems
203-***-****/United States
PROFESSIONAL SUMMARY:
• Python Backend Engineer with 5+ years of experience building scalable backend applications using FastAPI, Django, and Flask across enterprise environments.
• Strong experience developing RESTful APIs, microservices, and distributed backend systems supporting high-volume business workflows.
• Experience integrating LLM-based APIs and Generative AI services into enterprise applications to automate document processing, knowledge retrieval, and intelligent workflow execution.
• Hands-on experience developing backend services that integrate AI-driven capabilities with existing business systems using FastAPI, Django, and Microservices Architecture.
• Worked with retrieval-based architectures, prompt orchestration, embeddings, and semantic search techniques to improve information access and response relevance.
• Experience building scalable backend solutions supporting AI-enabled automation, enterprise integrations, and data-driven decision-making processes.
.• Strong experience processing large-scale datasets using PySpark, Databricks, Kafka, and ETL pipelines.
• Implemented Medallion Architecture (Bronze, Silver, Gold) and Delta Lake processing in distributed data environments.
• Skilled in asynchronous processing using Celery, Redis, RabbitMQ, and Kafka for scalable workflow execution.
• Experience developing event-driven backend systems supporting real-time integrations and distributed processing pipelines.
• Worked with relational and NoSQL databases including PostgreSQL, MySQL, MongoDB, DynamoDB, and Redis.
• Strong understanding of query optimization, indexing, caching strategies, and backend performance tuning.
• Skilled in AWS, Azure, Docker, Kubernetes, Terraform, Jenkins, and GitHub Actions for enterprise deployments and CI/CD automation.
• Experience implementing JWT authentication, RBAC, API security standards, structured logging, and monitoring solutions.
• Familiar with LangGraph-based orchestration concepts and multi-step AI workflow coordination for backend automation systems.
• Leveraged AI-assisted development tools such as ChatGPT, Cursor, and GitHub Copilot for debugging and development acceleration.
• Comfortable working in Agile/Scrum environments collaborating with cross-functional teams to deliver scalable enterprise platforms.
TECHNICAL SKILLS:
Category
Technology and tools
Backend Development
RESTful APIs, Microservices Architecture, API Design, FastAPI-based Services, Backend Systems, API Integration, Swagger/OpenAPI
Asynchronous
Celery, Redis, RabbitMQ, Kafka, Event-Driven Processing, Distributed Workflows
Languages & Frameworks
Python, Django, Scala, Flask, FastAPI, SQL, Java, Golang, HTML5, CSS3,C++
Cloud Services
AWS (EC2, S3, RDS, Lambda, API Gateway, CloudWatch, IAM), Azure(App Services, Azure SQL, Databricks),GCP
ETL & Orchestration
Azure Data Factory (ADF),AWS Glue, Apache Airflow, Azure Databricks, PySpark, Delta Lake, ETL Pipelines, Medallion Architecture (Bronze/Silver/Gold), Pandas, Data Processing
Databases & Storage:
PostgreSQL, MySQL, MongoDB, DynamoDB, Redis
Infrastructure & DevOps
Docker, Kubernetes, OpenShift, Terraform, Jenkins, CI/CD, Linux, GitHub Actions.
Authentication & Security
JWT Authentication, Role-Based Access Control (RBAC), API security,OAuth2,Structured Logging
Testing & Quality
PyTest, PyUnit, Unit Testing, Postman, CloudWatch, Grafana
AI / LLM Intergration:
OpenAI, Generative AI APIs, LLM Integration, RAG Pipelines, Prompt Orchestration, AI-powered backend services, LangGraph, Conversational API design and real-time response handling
Tools & Collaboration:
GitHub, Bitbucket, Jira, Agile/Scrum, Confluence, Cursor, ChatGPT, GitHub
WORK EXPERIENCE:
Xceedance, Aug 2024 – Till Date
Role:Python Backend Engineer
Responsibilities:
• Built and maintained scalable backend services using FastAPI and Django for insurance policy, claims processing, and AI-assisted business workflows.
• Designed and developed RESTful APIs supporting enterprise integrations, asynchronous processing, and high-volume backend operations.
• Designed and developed backend APIs supporting AI-powered insurance assistant, enabling users to retrieve policy details, claims information, and underwriting insights through natural language queries.
• Integrated OpenAI and Generative AI services with policy and claims platforms to automate document analysis, response generation, and knowledge retrieval processes.
• Built FastAPI-based inference endpoints to handle real-time AI requests, including prompt handling, response parsing, and fallback mechanisms..
• Worked on retrieval-based AI workflows using prompt orchestration, contextual response handling, semantic retrieval, and response grounding concepts.
• Implemented retrieval-based workflows using document chunking, embeddings, semantic search, and prompt orchestration techniques to improve response relevance..
• Worked with vector-search and embedding-based retrieval concepts to improve contextual AI interactions and intelligent backend search workflows.
• Implemented Redis caching and asynchronous processing strategies using Celery and Kafka to optimize AI response latency and backend scalability.
• Developed distributed backend services within a microservices architecture supporting AI-assisted workflows and real-time integrations.
• Built event-driven backend systems using Kafka, Celery, and Redis to support scalable processing pipelines and asynchronous task execution.
• Designed and maintained MongoDB collections for storing policy, claims, and AI interaction data, enabling flexible schema management for evolving business requirements.
• Optimized MongoDB queries and indexing strategies, reducing data retrieval time for policy and claims processing workflows
• Implemented JWT authentication and RBAC-based access control to secure APIs and enterprise backend services.
• Worked with containerized backend services using Docker and Kubernetes/OpenShift-based deployment environments supporting scalable enterprise applications.
• Monitored production systems using CloudWatch, structured logging, and debugging tools to improve backend reliability and issue resolution.
• Collaborated with frontend, DevOps, and product teams to support AI-driven integrations and scalable enterprise application releases.
• Wrote unit and integration tests using PyTest to improve application stability, deployment confidence, and backend reliability.
• Supported CI/CD pipelines using Jenkins and GitHub Actions to streamline automated builds and deployment workflows.
• Leveraged AI-assisted development tools such as ChatGPT, Cursor, and GitHub Copilot for debugging, documentation, and development acceleration.
• Explored LangGraph-based orchestration concepts and multi-step AI workflow coordination for backend automation systems.
Environment:
Python 3.x, FastAPI, Django, REST APIs, Microservices Architecture, OpenAI APIs, LLM Integration, RAG Concepts, Prompt Engineering, Semantic Retrieval, Kafka, Celery, Redis, PostgreSQL, MongoDB, Docker, Kubernetes, OpenShift, AWS (EC2, RDS, S3, API Gateway, CloudWatch), OpenShift, PyTest, Jenkins, GitHub Actions, Linux, Agile/Scrum
SS&C Technologies, May2022 – Aug 2023
Role: Python Engineer
Responsibilities:
• Developed and enhanced backend services using Django and Flask for financial applications and enterprise reporting systems.
• Designed and maintained RESTful APIs supporting internal business workflows, reporting platforms, and scalable backend integrations.
• Built data ingestion and ETL pipelines using Python, SQL, and PySpark to process large-scale structured and semi-structured financial datasets.
• Worked extensively with Azure Databricks and PySpark to support distributed data processing and enterprise analytics workflows.
• Implemented Medallion Architecture (Bronze, Silver, Gold) to organize raw, transformed, and curated datasets for analytics consumption.
• Utilized Delta Lake features including merge/upsert operations, incremental processing, and ACID-compliant data handling workflows.
• Processed high-volume datasets using Databricks, Kafka, and ETL pipelines to support reporting and AI/ML-driven workflows.
• Worked with large-scale data transformation pipelines supporting analytics, operational reporting, and backend processing systems.
• Optimized Spark jobs, SQL queries, and backend processing logic using partitioning, caching, and query tuning techniques.
• Built Python-based backend utilities and data workflows supporting enterprise automation and operational efficiency improvements.
• Implemented JWT authentication and RBAC-based access control to secure APIs and backend applications.
• Supported cloud-based deployments using Azure App Services and AWS environments for scalable backend operations.
• Developed asynchronous processing workflows and backend integrations supporting distributed enterprise systems.
• Worked with relational and NoSQL databases including PostgreSQL, MySQL, MongoDB, and Azure SQL for backend and reporting applications.
• Collaborated with analytics, QA, and product teams to deliver scalable backend and data processing solutions in Agile environments.
• Wrote unit tests using PyTest and participated in code reviews to improve application stability and deployment quality.
• Monitored backend processes, analyzed logs, and resolved production issues to improve system reliability and operational performance.
• Supported CI/CD workflows using Git, Jenkins, and deployment automation processes across enterprise environments.
• Leveraged AI-assisted development tools such as ChatGPT and GitHub Copilot for debugging, documentation, and development acceleration.
• Worked on backend workflows supporting data preparation and processing aligned with analytics and AI/ML-driven business use cases.
Environment:
Python 3.x, Django, Flask, REST APIs, PySpark, Azure Databricks, Delta Lake, Kafka, SQL, PostgreSQL, MySQL, MongoDB, Azure SQL, AWS, Azure App Services, JWT, RBAC, Docker, Kubernetes, Jenkins, Git, PyTest, Linux, Agile/Scrum, Jira.
HTC Global Services, Feb 2020 – Apr 2022
Role: Associate Python Developer
Responsibilities:
• Assisted in developing backend applications using Python, Django, and Flask for enterprise business applications.
• Supported REST API development and backend enhancements for internal systems and web-based applications.
• Worked on backend utilities and automation scripts using Python to support operational and reporting workflows.
• Assisted in building and maintaining backend services supporting business process automation and application integrations.
• Wrote SQL queries and supported database operations using PostgreSQL and MySQL for backend processing needs.
• Participated in data extraction, transformation, and validation workflows supporting reporting and analytics processes.
• Supported backend debugging, issue resolution, and production fixes by analyzing logs and application behavior.
• Worked on API testing, backend validation, and functional testing activities to improve application stability.
• Assisted in implementing structured error handling and backend validation logic for API reliability improvements.
• Gained exposure to cloud-based deployments and backend support activities within AWS environments.
• Worked with Git-based version control workflows and participated in collaborative development activities across shared codebases.
• Assisted senior developers in backend feature enhancements, release support, and production deployment activities.
• Participated in Agile/Scrum ceremonies including sprint planning, stand-ups, and task tracking activities.
• Supported unit testing and backend verification processes using PyTest and Postman tools.
• Collaborated with QA and frontend teams to support API integrations and application testing workflows.
• Assisted in maintaining backend documentation, code updates, and deployment support activities for enterprise applications.
• Gained exposure to containerized deployment concepts using Docker and enterprise backend infrastructure workflows.
• Leveraged AI-assisted development tools such as ChatGPT and GitHub Copilot for debugging, documentation, and learning acceleration.
Environment:
Python, Django, Flask, REST APIs, PostgreSQL, MySQL, SQL, Git, AWS (basic exposure), Linux, Pytest (basic), Postman, Agile/Scrum, Jira
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