Lead GenAI Engineer
About the role
The Lead GenAI Engineer role focuses on implementing real-world projects using Generative AI, fine-tuning large language models (LLMs), and Retrieval-Augmented Generation (RAG). The position involves developing enterprise-level agentic solutions with open frameworks, working with both open-source and proprietary LLMs, and utilizing vector databases and embeddings. Additional responsibilities include building Document AI/OCR/extraction solutions, applying various machine learning and AI techniques, and deploying solutions using modern UI and API tools. The ideal candidate will have strong proficiency in Python, PyTorch, SQL, and cloud environments, with at least 3 years of relevant experience and a bachelor's degree or higher in computer science or engineering.
Must have
- Experience in implementing real projects using Gen AI, Fine tuning LLMs , RAG. Experience in implementing enterprise level Agentic solution using any of the open frameworks (Langgraph, Google ADK, CrewAI, etc.)
- Understanding of Both Opensource and Open AI LLMs and RAG architecture Experience with vector databases/embeddings.
- Experience in Document AI / OCR / extraction solutions and structured output creation.
- Exposure to implementing multiple machine learning and AI techniques such as supervised, unsupervised, reinforcement learning, Deep learning, NLP Familiarity with NLP literature, thrust areas, conference venues, and code repositories Familiarity with UI tools like Streamlit, flask etc , FAST APIs , Rest APIs , Docker containers
- Proficiency in Python, pytorch , SQL and other programming languages for data analysis and modeling with atleast 3+ years of experience.
- Proficiency in libraries such as OpenAI API Proficiency with cloud development environments such as Azure, AWS, and GCP Proven good grasp of using LLMs, Deploying LLMs Proven excellent analytical and problem-solving skills, including the ability to disaggregate issues, identify root causes and recommend solutions
- Bachelor's degree or higher in computer science engineering.
Technologies
- Agentic AI
- AWS
- Azure
- CrewAI
- Deep Learning
- Docker
- Embeddings
- Fine-Tuning
- Flask
- GCP
- Generative AI
- LangGraph
- LLM
- Machine Learning
- NLP
- OpenAI
- Python
- PyTorch
- RAG
- Reinforcement Learning
- REST
- SQL
- Streamlit
- Vector Databases
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