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- Experience with Programming (Python, SQL, NoSQL, Spark) and ML tools and cloud technologies (AWS, Azure, GCP).
- Deep understanding of ML algorithms for common use cases in both structured and unstructured data systems.
- Experience with GenAL/ LLM tools: LangChain, RAG pipelines, Embeddings & vector databases, Multi-modal LLMs, Fine-tuning techniques
- Experience in building FastAPI-based services and Dockerized deployments
- Experience building production-grade GenAI solutions
- Familiarity with:
- Multi-agent architectures and orchestration
- Exposure to enterprise analytics / BI tools:
- Power BI, Tableau, or other visualization tools
- Experience collaborating with UI/UX teams for AI-driven solution delivery
Responsibilities
- Build and deploy enterprise-grade Generative AI and ML solutions
- Design scalable AI systems enabling natural language–driven automation and insights
- Develop and implement GenAI workflows for enterprise use cases
- Build LLM-powered applications for:
- Analytics & reporting
- Document intelligence
- Recommendation systems
- Design and implement advanced RAG (Retrieval-Augmented Generation) pipelines including:
- Embeddings
- Vector search
- Ranking and grounding mechanisms
- Build and expose AI capabilities via REST APIs and microservices
- Deploy, monitor, and manage solutions using Docker and cloud platforms
- Implement output validation, monitoring, and evaluation frameworks for production reliability
Qualifications
- Strong understanding of AI system evaluation, monitoring, and guardrails
- B.Tech / B.E. (Preferably from Tier-1 institutes)