- 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 3 GenAI/Agentic AI projects in production, ensuring reliability, scalability, and performance.
- Integrate SQL, No-SQL, and vector databases such as Postgres, MongoDB, and ChromaDB.
- Implement graph databases (Neo4j) for knowledge graph-based use cases.
- Provide technical leadership and mentorship to engineering teams.
- Collaborate with cross-functional stakeholders to identify and deliver GenAI solutions across multiple business domains.
- 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.
- 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.
- Coordinate team tasks, assist in project management, handle customer inquiries, and prepare reports.
- 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.
- 3+ years of hands-on experience in Python with strong 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.
- Bachelor's/Master's in Engineering 2-5 years
Job description provided by EXL Service.
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