Senior Python Backend Engineer with AI Enablement

Bhavana Reddy Maram

Python Developer

984-***-**** *************@*****.*** Morrisville, NC https://www.linkedin.com/in/bhavana-01-m/

PROFESSIONAL SUMMARY

Python Backend Developer with 5+ years of experience building scalable API platforms and AI-enabled backend systems in regulated financial environments. Experienced in designing microservices, cloud-based data pipelines, and semantic search infrastructure that support enterprise research, compliance, and transaction monitoring workflows. Strong background in RAG-based LLM application development, vector search, asynchronous processing, and secure data access controls, with hands-on experience deploying production systems on AWS and integrating enterprise-governed OpenAI services into backend platforms.

TECHNICAL SKILLS

Programming and Backend: Python, Django, FastAPI, Flask, AsyncIO, SQLAlchemy, Pydantic, REST API design

AI Enablement: LangChain, LlamaIndex, RAG architecture, vector search (FAISS, OpenSearch), embedding lifecycle management, OpenAI integration, structured output validation, RBAC-aware retrieval, audit logging design

Databases: PostgreSQL, MySQL, SQLite, MongoDB, Redis

Messaging & Data: Apache Kafka, Pandas, NumPy

Cloud & DevOps: AWS (EC2, Lambda, RDS, S3, CloudWatch), Azure, Azure AD (RBAC), exposure to GCP

Testing & Tools: PyTest, UnitTest, Postman, Git, GitHub, Jenkins

Practices: CI/CD, TDD, Agile/Scrum

EDUCATION

Master of Science in Computer Science (University of Central Missouri) Jan 2023 – May 2024

PROFESSIONAL EXPERIENCE

Morgan Stanley, USA Python Developer (AI enablement / LLM Systems) Jan 2024 – Present

●Worked on backend engineering for a production AI document intelligence platform serving Investment Research, Compliance Advisory, and Internal Audit teams within a regulated capital markets environment.

●Built backend services for a FastAPI-based RAG platform enabling semantic search across 400K+ research and compliance documents used by investment research and audit teams.

●Integrated OpenAI APIs with enterprise governance controls including prompt standardization, Pydantic-based schema validation, audit logging, and API gateway enforcement to ensure traceable and compliant LLM inference.

●Implemented Kafka-based ingestion pipelines processing 5K–10K documents daily, orchestrating document parsing, chunking, embedding generation, and vector synchronization with AWS-backed storage and processing services.

●Optimized semantic query performance using Redis caching and AsyncIO concurrency, reducing p95 retrieval latency by ~30% under concurrent analyst workloads.

●Designed and implemented backend services on AWS using EC2 for compute, S3 for document storage, and RDS for metadata management, supporting the ingestion and retrieval of 400K+ research documents in the FastAPI-based RAG platform while leveraging CloudWatch for monitoring and operational visibility.

●Established retrieval evaluation pipelines and citation mapping frameworks to measure semantic relevance and ensure grounded, citation-backed AI responses.

●Maintained CI/CD pipelines using Jenkins, supporting containerized deployments and automated testing while leveraging AWS CloudWatch for production monitoring and operational alerting.

●Delivered secure AI-powered document search capabilities aligned with enterprise compliance and audit requirements across research and governance teams.

Standard Chartered Bank, India Python Developer Mar 2019 – Dec 2022

●Developed backend systems supporting enterprise banking workflow platforms used by Operations, Risk Control, and Compliance divisions across transaction validation and regulatory enforcement processes.

●Built Django, Flask, and FastAPI backend services powering operational dashboards, payment validation APIs, and approval workflows used by compliance and transaction monitoring teams.

●Improved transaction processing reliability using asynchronous processing, idempotency controls, and retry mechanisms for high-volume payment validation workflows.

●Designed rule-evaluation engines handling routing rules, risk thresholds, and approval dependencies, with caching layers to accelerate repeated validations.

●Automated compliance validation pipelines using Pandas and NumPy to detect misconfigured approval paths and translate regulatory requirements into deterministic backend rules, reducing production incidents by ~20%.

●Developed PyTest test suites covering positive, negative, and edge scenarios and contributed to CI/CD automation and backend refactoring to improve platform stability and maintainability.

●Deployed Python backend APIs on AWS infrastructure using EC2-hosted services with RDS-backed relational databases, integrating S3 for secure storage of processing artifacts and using CloudWatch logs for monitoring API performance and operational health within enterprise banking workflows.

●Supported audit-sensitive transaction workflows with traceability, change logging, and regulatory reporting controls to strengthen governance and reduce compliance risk.

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