NBBL- AI Engineer

The opportunity

Lead AI projects and drive growth of the NBBL products by building and managing strategic AI agents and solutions. Own AI delivery, expand possibilities, and enable co-creation of innovative payment solutions while ensuring seamless execution through cross-functional collaboration. Design, build, and deploy autonomous AI agents that can reason, plan, use tools, and execute multi-step workflows. Will work at the frontier of LLM-based systems, building production-grade agents that integrate with internal tools, APIs, and enterprise data to automate complex business processes. .

Job details

Job Title: AI Engineer

Years of Experience: 7 yrs+

Education: BE/B.Tech / ME/M.Tech / MCA / MSc or equivalent (AI/ML/Data Science/Computer Science or related fields preferred)

Full-time

Location: Mumbai/ Hyderabad/Chennai

Permanent Role

Key responsibilities

1. Design and develop single- and multi-agent systems using frameworks like LangGraph, LangChain, CrewAI, AutoGen, or similar.

2. Build agent capabilities including tool use, planning, memory, reflection, and human-in-the-loop workflows.

3. Integrate agents with external systems via APIs, MCP (Model Context Protocol) servers, and custom connectors.

4. Implement RAG pipelines using vector databases (Pinecone, Weaviate, Qdrant, Chroma, etc.) for context-aware agent behavior.

5. Optimize prompts, agent loops, and orchestration for accuracy, latency, and cost.

6. Build evaluation harnesses, guardrails, and observability for agent performance (LangSmith, Langfuse, Arize, or similar).

7. Collaborate with product, data, and engineering teams to identify high-value agentic use cases and ship them to production.

8. Stay current with advances in LLMs, reasoning techniques, and agent architectures.

Key skills and experience required

1. Strong Python programming skills, including async patterns.

2. Hands-on experience building LLM-powered applications with frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen.

3. Solid understanding of agent design patterns: ReAct, planning, tool use, multi-agent collaboration, and memory.

4. Experience with leading LLM APIs (Anthropic Claude, OpenAI, Google Gemini, open-source models).

5. Working knowledge of RAG, embeddings, and vector databases.

6. Prompt engineering and structured output techniques (function calling, JSON schemas, tool definitions).

Experience deploying production systems on AWS / Azure / GCP, with Docker and CI/CD.

8. Experience with MCP (Model Context Protocol), browser-use, or computer-use agents.

9. Fine-tuning, LoRA, or model distillation experience.

10. Knowledge of evaluation frameworks (RAGAS, DeepEval, custom evals).

11. Experience with workflow orchestration (Temporal, Airflow, Prefect). 12. Familiarity with safety, alignment, and red-teaming practices for LLM systems.

13. Background in NLP, ML, or applied research.