Agentic AI Developer

Role Overview :

Key Responsibilities:

• Lead the development and productionization of robust, scalable, and secure agentic AI systems and multi-agent frameworks.
• Design and implement intricate agent workflows (reasoning, planning, tool use, and collaboration) to solve complex, real-world problems.
• Oversee the integration of LLMs, external APIs, and vector databases to build highly functional, autonomous agents with strong performance guarantees.
• Develop and refine strategies for agent memory, state management, and continuous learning in production environments.
• Champion responsible AI practices, ensuring systems adhere to principles of fairness, transparency, governance, and compliance with enterprise security standards. Implement observability, monitoring, and auditing mechanisms to guarantee reliability, accountability, and trust in AI systems.
• Architect and manage cloud-native deployments leveraging Docker, Kubernetes, and MLOps best practices for high availability and scalability.
• Collaborate with security and compliance teams to enforce data governance, access controls, and secure integration patterns. Mentor and guide junior developers, providing technical leadership and setting best practices for agentic AI development.
• Partner with product managers and engineering teams to define roadmaps for production AI systems aligned with business objectives.
• Benchmark, optimize, and continuously improve agent performance in real-world, mission-critical applications.
• Stay at the forefront of research in agentic AI, LLMs, and Responsible AI, and propose innovative approaches for production deployment.

Required Skills

Experience: 5 11 years in software development with strong focus on AI, ML, or distributed systems.

Programming: Expert-level proficiency in Python.

Agentic AI: Proven experience in designing and deploying agentic systems using LangChain, LlamaIndex, AutoGen, or similar frameworks.

LLMs & Vector DBs: Hands-on experience with LLMs (OpenAI, Anthropic, Google etc.) and vector databases (Pinecone, Milvus, OpenSearch etc.) for Advanced RAG and semantic search.

Production AI: Deep experience in productionizing GenAI applications with a focus on governance, observability, and compliance.

Cloud & DevOps: Proficiency in cloud platforms (AWS, Azure, GCP), containerization (Docker), and orchestration (Kubernetes) for scalable, reliable AI deployments.

Security & Governance: Strong knowledge of Responsible AI principles, model governance, data security, and compliance frameworks.

System Design: Solid background in large-scale, distributed system design and optimization.

Communication: Excellent mentoring, and stakeholder communication skills.

(1.) To develop and guide the team members in enhancing their technical capabilities and increasing productivity

(2.) To prepare and submit status reports for minimizing exposure and risks on the project or closure of escalations.

(3.) To be responsible for providing technical guidance / solutions ;define, advocate, and implement best practices and coding standards for the team.

(4.) To ensure process compliance in the assigned module| and participate in technical discussions/review as a technical consultant for feasibility