Key Responsibilities:

- Design, develop, and implement Agentic AI systems capable of autonomous decision-making and task execution.

- Build and optimize AI/ML models leveraging LLMs, NLP, and advanced AI techniques.

- Develop AI-driven applications using RAG (Retrieval-Augmented Generation) and agent-based frameworks.

- Integrate LLMs into enterprise applications and workflows.

- Monitor team performance, track deliverables, and ensure adherence to timelines and quality standards.

- Provide technical leadership, guidance, and hands-on support to team members.

- Conduct regular team coaching and mentoring sessions to enhance team capability and performance.

- Collaborate with cross-functional teams including Product, Engineering, and Business stakeholders.

- Drive best practices in AI development, model lifecycle management, and governance.

- Identify risks, troubleshoot technical challenges, and ensure smooth project execution.

Required Skills & Qualifications:

- 2+ years of hands-on experience in AI application development, including:

1. Agentic AI systems

2. RAG (Retrieval-Augmented Generation)

3. Tools & LLM-based applications

- Strong programming skills in Python with experience in HTTP/REST APIs.

- Hands-on experience with AI agent frameworks such as LangChain, LangGraph, or similar.

- Experience in LLM integration and prompt engineering.

- Experience working with vector databases suchs as Qdrant, Chroma.

- Exposure to Knowledge Graphs (e.g., Neo4j).

- Experience with cloud platforms such as AWS, Azure, or GCP.

- Strong experience in ML/DL model development, including training, fine-tuning, and evaluation.

- Proven experience in team monitoring, performance tracking, and delivery management.

- Demonstrated ability in mentoring, coaching, and leading engineering teams.

- Strong analytical, problem-solving, and communication skills.