About this role
• 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.