Agentic AI Engineer

Responsibilities :

Design and implement production-grade agentic architectures leveraging LLMs and reasoning engines.

Build and optimise multi-agent systems for complex task orchestration and collaboration.

Integrate external tools, APIs, and knowledge bases for robust agent augmentation.

Tune agent performance for autonomy, adaptability, reliability, and safety.

Embed agentic AI into enterprise workflows in collaboration with product teams.

Prototype advanced capabilities: persistent memory, goal planning, and self-correction loops.

Contribute to code reviews, technical documentation, and team best practices.

Additional Responsibilities:

Good to Have

Exposure to MLOps practices †experiment tracking, model versioning (MLflow, DVC).

Knowledge of distributed systems and scalable backend architectures.

Experience with cloud AI services on Azure, AWS, or GCP.

Technical and Professional Requirements:

Strong Python programming with hands-on AI/ML framework experience.

Working knowledge of LLMs, prompt engineering, and basic fine-tuning (LoRA/QLoRA).

Practical experience with agentic frameworks such as LangChain, AutoGen, or CrewAI.

Experience with API integration and tool-augmented agent development.

Understanding of responsible AI, safety guardrails, and LLM evaluation strategies.

Familiarity with vector stores, retrieval systems, and RAG architectures