AI Engineer

Responsibilities

• Design and architect multi-agent AI systems (task planning, tools, memory, context handling).
• Create agent workflows using tools like Lang Graph, Autogen, CrewAI, Kore.ai Workbench, etc.
• Build and define RAG pipelines (chunking, vector stores, scoring, context optimisation).
• Set up guardrails to reduce hallucinations and ensure reliable outputs.
• Improve system performance, reduce latency, control costs, and scale applications.
• Guide and mentor engineers; ensure good design, implementation, and documentation.
• Work closely with product and customer teams to convert business workflows into agent-based solutions.
• Build and implement agent workflows, prompts, and tool integrations.
• Create system-level and role-based prompts; handle function/tool calling.
• Develop vector store pipelines using Mongo
  
  DB, Click House, or external vector DBs.
• Add monitoring, logging, and evaluation metrics for observability.
• Help build reusable internal libraries/frameworks for agent orchestration.
• Stay updated with new AI technologies and trends.

• 4-10 years of experience in software engineering.
• Practical, hands‑on experience in agentic AI development (not just basic API use).
• Strong knowledge of LLM prompting, tool calling, reasoning optimisation, and context management.
• Experience with Lang Chain, Lang Graph, Autogen, Kore.ai Agent Workbench, or similar tools.
• Good understanding of RAG systems, embeddings, vector databases, semantic search, models, and chunking.
• Strong coding skills in Python or Node.js
• Experience working with cloud‑native and distributed systems.
• Experience integrating with enterprise platforms like Service Now, Workday, or SAP.
• Knowledge of LLM observability and evaluation tools.
• Familiarity with Kubernetes, Docker, CI/CD, and scalable deployment pipelines.