Key Responsibilities
- Research and implement Generative AI use cases using LLMs (text, voice, image, or multimodal).
- Build prototypes using frameworks such as LangChain, LlamaIndex
- Design and optimize prompts for accuracy, safety, and performance.
- Implement RAG (Retrieval‑Augmented Generation) pipelines using vector databases.
- Integrate AI models via APIs or open‑source models (OpenAI, Azure OpenAI, Hugging Face, local LLMs).
- Work on AI agents, tools, and orchestrations (function calling, workflows).
- Evaluate model outputs using metrics like relevance, hallucination rate, latency, and cost.
- Assist in deploying GenAI applications using Docker and cloud platforms preferably Azure.
- Collaborate with product, data, and engineering teams
- Document experiments, findings, and best practices.
- Mentor engineers and drive best practices
Requirements
Required Skills
- 3+ years of professional experience and 2+ years of experience in GenAI.
- Strong experience in Python or Java
- Hands-on with LLMs, prompt engineering, and RAG
- Experience with LangChain, LlamaIndex, or similar
- Knowledge of vector databases (FAISS, Pinecone, etc.)
- Experience with cloud platforms (Azure preferred)
- Understanding of microservices and distributed systems
- Basic experience with Git and REST APIs.
Ability to read research blogs or documentation and implement ideas quickly.
Benefits
Benefits
- Hands‑on exposure to modern GenAI stacks
- Certificate / Letter of Recommendation
- Access to real‑world datasets and use cases
