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AI/ML Engineer with 4+ years in GenAI & LLM Systems
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI / ML Engineer with 4 years of experience building production-grade GenAI systems, LLM-powered pipelines, and intelligent full stack applications. Deep expertise in RAG architecture, LangChain orchestration, vector embeddings, semantic search, and multi-agent AI system design. Proficient in Python (FastAPI, TensorFlow, PyTorch, Scikit-learn) for ML model development and serving, paired with React / Node.js full stack delivery and AWS cloud infrastructure. Passionate about solving real-world business problems with AI — with measurable results at enterprise scale.
MGM Engineering College, Nanded
Bachelor of Engineering · Computer Science
N/A – June 30, 2021
Indira Institute of Technology, Nanded
Diploma · Computer Science
N/A – Present
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Enterprise AI Knowledge Assistant
June 16, 2026 – Present
End-to-end RAG architecture: document ingestion (PDF, DOC, CSV) → text chunking → vector embedding (OpenAI Embeddings) → FAISS semantic search → LangChain context assembly → streamed LLM response — cutting enterprise knowledge retrieval time by 70%. LLM context window management and multi-turn conversation memory — enabling coherent, stateful dialogue across long user sessions with accurate source-backed responses. Multi-agent system: HR Bot, Support Bot, Admin Bot — each with specialised prompt templates, tool bindings, and RBAC-controlled access; deployed as independent FastAPI microservices on AWS. ML model serving layer with FastAPI + Pydantic — async inference endpoints, request validation, response streaming, and SQLAlchemy-backed session persistence. All services containerised with Docker + Docker Compose (Poetry for Python deps); GitHub Actions CI/CD for automated testing and zero-downtime AWS deployment. React + TypeScript frontend with real-time WebSocket streaming — ChatGPT-class progressive rendering, file upload UI, role-based dashboards, and Tailwind CSS design system.
Cultural Fit Analysis
The candidate's experience spans freelance, full-time, and senior roles across different companies, showcasing adaptability and a broad exposure to various work environments. Their involvement in building diverse AI applications (knowledge assistants, code review systems, BI platforms) and full-stack development indicates a versatile skill set and a willingness to tackle varied challenges. The emphasis on measurable outcomes and leading technical decisions aligns well with a performance-driven culture. The breadth of technologies and frameworks used suggests a continuous learning mindset, which is a strong cultural fit for innovative teams.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and a results-oriented approach, consistently highlighting quantifiable achievements. Their experience in leading architecture decisions and mentoring suggests good collaboration and leadership potential. The diverse project portfolio indicates adaptability and a proactive learning attitude, which are crucial for fast-paced AI/ML environments. The focus on end-to-end system delivery, from backend ML services to streaming frontends, shows a comprehensive operational understanding.