Agentic AI Engineer for RAG Systems
Resume:
Mohd Sameed Qureshi
AI Engineer with a strong track record in developing and deploying multi-agent systems and retrieval-augmented generation (RAG) pipelines. Proficient in Python and FastAPI, I have successfully integrated large language models (LLMs) into existing software architectures, automating workflows and enhancing operational efficiency. My experience includes mentoring teams to improve onboarding processes and ensuring effective integration of AI solutions into mission-critical applications.
EXPERIENCE
AI/ML Developer — Tata Consultancy Services
Nagpur, India Jan 2025 – Present
●Led the design and development of intelligent web applications, creating production-quality Python (Flask) backends and React frontends integrated with Generative AI capabilities, resulting in a 30% increase in application performance.
●Developed a multi-agent GenAI platform that automates the software development lifecycle (SDLC) using LLM-based agents and RAG, enhancing workflow efficiency by reducing manual intervention by 70%.
●Conducted Knowledge Transfer (KT) sessions and mentored new team members, facilitating faster onboarding and effective team integration, which improved team productivity by 25%.
PROJECTS
Agentic AI for Software Engineering
●Designed and implemented a multi-agent GenAI platform that automates the end-to-end software development lifecycle using LLM-based agents and RAG architecture, achieving a 90% reduction in manual effort for user story management.
●Product Owner Agent: Generates, refines, and updates user stories for Jira using semantic search and RAG, significantly reducing manual effort by 80-90%.
●Developer Agent: Produces production-ready code from structured user stories, minimizing human error by 80% and accelerating deployment timelines.
●Tester Agent: Creates test cases and scripts from user stories, improving test coverage and cutting manual QA efforts by 50%.
●Implemented artefact ingestion, embedding generation, and semantic search to ensure outputs are context-aware and aligned with domain requirements.
Automation of SVN to GitLab Migration
●Streamlined the manual migration process, reducing efforts by 70-80% and ensuring a seamless transition between platforms.
●Developed and validated an agentic solution for migrating repositories from SVN to GitLab without data loss, ensuring validation at each migration step, which improved data integrity.
●Provided comprehensive analysis reports on repository structure before and after migration, enhancing clarity and understanding of the process.
●Orchestrated migration agents to handle various repository sizes (small, medium, large), optimizing the migration process based on user queries, resulting in a 50% faster completion time.
EDUCATION
Shri Ramdeobaba College of Engineering and Management
Electronics Engineering – Jan 2024
Namo Narayani Junior College
Mathematics & Science – Jan 2019
SKILLS
Languages: Python
Frameworks & Libraries: FastAPI, LangChain, LangGraph
Tools & Platforms: Google ADK, OpenAI, Hugging Face, Gemini, Docker, Terraform, Git
Databases: Neo4j, FAISS
Cloud & DevOps: Azure, AWS
Concepts: Multi-Agent Systems, RAG pipeline, Semantic Search, Prompt Engineering, Agentic workflow, MLOps, LLMOps
Testing & Methodologies: Evaluation, Validation
Soft Skills: Collaboration, Communication
CERTIFICATIONS
●Cloud Developer Certification
Contact this candidate