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 helpful, but understanding the underlying architecture is more important than knowing a particular framework.

What we value

- Strong 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