Assistant Manager

• 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.