EnhanceLearning.
AI is looking for a hands-on Agentic AI Engineer to design, build, and evaluate AI-native and agentic systems. This is an engineering role for someone who wants to go beyond simple LLM applications and work on the architecture behind production-grade AI systems. You will work across AI agents, LLMs, tools, MCP, memory, orchestration, evaluation, observability, reliability, and security.

What you'll do
• Design and build AI agents, agentic workflows, multi-agent systems.
• Integrate LLMs from multiple providers and work with open-source models.
• Build tool/function-calling integrations and MCP servers.
• Build evaluation and testing approaches for AI systems.
• Develop observability and tracing for agentic workflows.
• Investigate reliability, failure modes, and recovery strategies.
• Design security and permission boundaries for agents and tools.
• Optimize AI applications for latency, reliability, scalability, and cost.
• Build prototypes and turn successful experiments into reusable implementations.
• Research emerging AI technologies, frameworks, protocols, and architecture patterns. What we're looking for
• 3–7 years of software engineering experience.
• Strong programming skills in Python and/or TypeScript.




• Strong understanding of software architecture and system design.
• Hands-on experience building applications using LLMs.
• Experience with AI agents or agentic workflows.
• Understanding of RAG, embeddings, vector databases, and context management.
• Experience with function/tool calling and API integrations.
• Strong debugging and problem-solving ability. Preferred experience Experience with one or more of:
• LangGraph
• LangChain
• LlamaIndex
• OpenAI Agents SDK
• MCP
• A2A
• Microsoft Agent Framework, Google ADK
• Multi-agent architectures
• Event-driven architectures
• Docker / Kubernetes
• PostgreSQL / Redis
• AWS / GCP / Azure
• LLM evaluation and testing frameworks Framework experience is useful, but understanding the underlying architecture is more important than knowing a particular framework. What we value
• Solid engineering fundamentals
• Architectural thinking
• Curiosity and continuous learning
• Hands-on experimentation
• Ability to work independently
• Willingness to challenge assumptions
• Focus on reliability and maintainability

📌 Agentic AI Engineer (India)
🏢 EnhanceLearning.AI
📍 India