Key Responsibilities

- Lead the design and implementation of agentic AI workflows using LangGraph, AutoGen, LangChain, or similar frameworks.

- Architect and optimize scalable APIs (REST/WebSocket) for production deployment.

- Develop, fine-tune, and integrate large language models into enterprise applications.

- Deploy and maintain at least 2 GenAI/Agentic AI projects in production, ensuring reliability, scalability, and performance.

- Integrate SQL, No-SQL, and vector databases such as Postgres, MongoDB, and ChromaDB.

- Provide technical leadership and mentorship to engineering teams.

- Collaborate with cross-functional stakeholders to identify and deliver GenAI solutions across multiple business domains.

- Experience in multi-agent architecture.

- Ensure robustness, security, and compliance of deployed AI systems.

- Stay current with trends in GenAI, deep learning, and orchestration frameworks.

- Document and present technical solutions to both technical and non-technical audiences.

Required Qualifications

- Bachelor’s or Master’s degree in Computer Science, Data Science, Data Engineering, AI/ML, or related field.

- 4+ years of total professional experience in AI/ML, Data Science, or related fields.





- 4+ years of hands-on experience in Python with robust software engineering practices.

- At least 3 years of experience in AI/ML engineering, including 2+ years of hands-on experience in Generative AI /Agentic AI.

- Hands-on experience with deep learning frameworks (PyTorch, TensorFlow).

- Expertise in large language models (LLMs), prompt engineering, and fine-tuning.

- Proven track record of deploying at least 3 GenAI/Agentic AI projects into production.

- Strong background in Data Science or Data Engineering, including data pipelines, ETL, and data modeling.

- Practical knowledge of SQL, No-SQL, vector databases, and graph databases.

Preferred Qualifications

- Familiarity with LangGraph, AutoGen, LangChain, or similar agentic AI frameworks.

- Experience with multi-agent systems and orchestration of intelligent workflows.

- Knowledge of MLOps practices (CI/CD, model monitoring, retraining pipelines).

- Strong problem-solving skills and ability to thrive in fast-paced R&D; environments.

- Contributions to open-source AI/ML projects or published research in generative AI.

📌 GenAI Engineer - NCR/Bangalore (Bengaluru)
🏢 EXL
📍 Bengaluru