TechGrove is the Centre of Excellence for Banyan Software, based in Chennai, India. It plays a key role in supporting Banyan's global businesses through technology, security, and software development. TechGrove brings together India's deep pool of technical talent with Banyan's long-term approach to growth, creating a trusted, developer-focused environment where people can do their best work.

Principal AI Engineer

- TechGrove AI Engineering Pod_

You build the intelligent features that make software feel like magic, and you make them work in production. You are equally at home shipping a clean API, standing up a RAG pipeline, evaluating an agent, or tuning guardrails so an LLM behaves safely at scale. You are excited by the current moment in AI and pragmatic about where it actually adds value.

If you want to turn the frontier of applied AI into shipped, dependable product features, this role is for you.

About the Pod : A TechGrove AI Engineering Pod is a self-contained, AI-native software delivery team that we embed inside one of our Operating Companies (OpCos) to build, modernize, and ship production software. Each pod pairs senior engineering talent with agentic AI tooling such as Claude Code, plus the accelerators of Banyan's AI Application Modernization Factory, to deliver at a velocity and quality bar a traditional team cannot match.

A pod is typically four to eight people, and OpCos add more pods as their ambitions grow. You will work as part of a tight, high-trust team with real ownership of what you build. Pods are delivered from Banyan's India-based TechGrove.

What You Will Do :

- Orchestrate and integrate LLMs :Implement LLM orchestration, prompt and context engineering, guardrails, and evaluation to deliver reliable, safe, production-grade AI.




- Engineer cloud-native services :Build scalable, secure, resilient services that host and serve AI capabilities, as a strong hands-on contributor.
- Build agents and tooling :Develop AI agents and tooling, including building and connecting MCP servers and tools, and champion agentic development practices across the pod's SDLC.
- Own data and retrieval :Design the data, retrieval, and vector or knowledge-graph infrastructure that powers GenAI features, with quality, performance, and security built in.
- Own technical design and quality :Lead design and implementation for AI features, write clean, well-tested code, and uphold engineering standards, security, and observability.
- Collaborate and mentor :Partner with the Architect and senior engineers, and coach the team on AI/ML engineering best practices.

What You Bring :

- Experience :8+ years in software engineering, with significant hands-on experience implementing ML/AI features in production.
- Applied AI/ML :Demonstrated experience implementing GenAI capabilities (RAG and GraphRAG, LLM orchestration, prompt and context engineering, guardrail design) and/or ML features in production systems.
- Agentic and GenAI frameworks :Hands-on experience with GenAI frameworks (LangChain, LlamaIndex), multi-agent frameworks (crewAI, AutoGen, or similar), and developing AI agents and MCP servers and tools.




- Software engineering depth :Strong full-stack ability with solid SQL/No-SQL and API design skills.
- Modern tech stacks :Hands-on production experience with at least two of:.NET Core, Python, TypeScript, and Java.
- AI-native tooling :Fluency with AI-assisted development tooling (Claude Code or similar) as a daily, core part of your workflow.
- DevSecOps :Experience with CI/CD (GitHub Actions, GitLab CI) and cloud-native observability.
- Collaboration :Strong communication and an ownership mindset, comfortable with ambiguity and rapid change.
- Education :Bachelor's degree in Computer Science or a related technical field, or equivalent practical experience.

Nice to Have :

- Deep LLM and vector database expertise: hybrid vector databases, knowledge graphs, and advanced prompt and guardrail design for enterprise AI safety.
- Experience fine-tuning or evaluating models, building eval harnesses, or implementing LLM observability.
- Experience building and operating multi-tenant SaaS at scale.

What Success Looks Like :

- First 90 days :You have shipped your first production AI feature, established reliable patterns for orchestration, retrieval, and evaluation, and the pod has a clear, protected approach to building with LLMs.
- First 6 months :AI capabilities are a dependable, differentiating part of the product, with measured quality and guardrails, and the team is meaningfully more capable in applied AI because of your mentorship.

Why TechGrove, Why Banyan :

- Permanent home, long-term thinking :Banyan acquires great software businesses and keeps them for good. We invest for the long term, which means you build software meant to last, not to flip.
- Real ownership :You own outcomes end to end inside a small, senior team. The

📌 Principal Ai Engineer (Bengaluru)
🏢 TechGrove by Banyan Software
📍 Bengaluru