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AI Engineer with 4+ years in Generative AI & Machine Learning
Highly skilled AI Engineer with 4.7 years of experience in developing Generative AI and Physical AI systems. Proficient in crafting scalable GenAI pipelines involving RAG and fine-tuning, and experienced in deploying solutions across cloud and edge devices. Adept at integrating LLMs, VLMs, and multimodal perception for autonomous decision-making and intelligent automation. Proven track record in full-stack development, database management, and leading complex projects such as AI knowledge graph chatbots and autonomous drone navigation.
Pandit Deendayal Energy University
Bachelor of Technology · Computer Engineering
August 1, 2019 – June 30, 2023
Knowledge High School
H.S.C
June 1, 2018 – May 31, 2019
Capgemini India
Analyst II – AI Engineer
June 1, 2024 – Present
Bengaluru, Karnataka, India
Bisag-N, Ministry of Electronic and IT
Software Developer
February 1, 2024 – May 1, 2024
Delhi, Delhi, India
Capgemini India
Analyst II – AI Engineer
August 1, 2019 – May 1, 2023
Gandhinagar, Gujarat, India
AI knowledge graph chatbot for medical device documentation
May 1, 2023 – February 1, 2024
Built a production hybrid RAG chatbot combining Neo4j knowledge-graph traversal with FAISS vector search across 5 medical device product lines, replacing manual document search for support engineers. Orchestrated the pipeline as a LangGraph state machine (guardrail → retrieve → rerank → generate), serving a fine-tuned Qwen LLM and embedding model as independent microservices via KServe. Engineered a self-improving feedback loop that embeds user corrections into a dedicated vector index and automatically resurfaces them for similar future questions, with confidence-weighted logic to override LLM answers.
Unitree G1 Reinforcement Learning
May 1, 2023 – February 1, 2024
Designed and trained reinforcement learning (RL) policies for the Unitree G1 humanoid robot enabling locomotion and manipulation tasks such as walking, dancing, and pick-and-place operations. Developed an LLM-powered conversational chatbot integrated with person identification, enabling the robot to recognize individuals and engage in context-aware, personalized interactions. Integrated vision-based person identification and dialogue management to allow real-time human-robot interaction.
Agentic Autonomous Drone Navigation Using VLM and LLM
May 1, 2023 – February 1, 2024
Implemented a persistent semantic memory system using LangChain, Chroma vector store, and HuggingFace sentence embeddings to store and retrieve vectorized observations including rooms, directions, odometry, and detected exits. Designed an agentic reasoning pipeline integrating Vision-Language Models (VLMs) and Large Language Models (LLMs) for scene analysis, room inference, and high-level decision making. Enabled goal-directed indoor navigation by combining semantic memory retrieval with multimodal reasoning to determine the most optimal action toward a specified target room.
End-to-End Machine Learning, Deep Learning and NLP
Krish Naik
June 1, 2026 – Present
Synthetic Data Generation
NVIDIA
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong interest and practical application in cutting-edge AI domains, including robotics, natural language processing, and computer vision. This diversity of projects, from medical device documentation to humanoid robot control and autonomous drone navigation, suggests a broad intellectual curiosity and adaptability. The experience at Capgemini as an AI Engineer, both current and previous, aligns well with a professional environment focused on advanced AI solutions. The candidate's skills breadth in programming, AI/ML frameworks, and DevOps/databases indicates a well-rounded technical profile suitable for diverse team environments.
Soft Skills & Operational Fit
The candidate's project descriptions indicate strong problem-solving skills and an ability to work on complex, multi-faceted AI challenges. The 'self-improving feedback loop' and 'agentic reasoning pipeline' projects suggest an innovative and proactive approach to system design. The English test score of 76 indicates good communication clarity, which is essential for technical collaboration and documentation. The psychometric test score is 0, which means there is no data to evaluate logical reasoning, work attitude, stress handling, or team collaboration.