Generative AI Developer

Job description

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. **Required Qualifications** - Bachelors 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