Staff Software Engineer - AI

Banyan Software; India; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

TechGrove, the Centre of Excellence for Banyan Software, based in Chennai, India, seeks a hands-on Staff Engineer. You will spend ~90% on backend work in Java or Go—distributed systems, data pipelines, AI and agent infrastructure—and ~10% on frontend work in React.

You’ll own core platform systems and the data layer with PostgreSQL, ClickHouse, OpenSearch, and Redis, with Docker and Kubernetes for deployment.

Qualifications

• Hands-on experience building with LLMs, including embeddings, vector or hybrid search, and retrieval evaluation.
• Strong backend focus with Java or Go and distributed systems, memory management and performance.
• Experience with data stores at scale: PostgreSQL, OpenSearch/Elasticsearch or Redis.
• Ability to design and ship AI-native features end-to-end and evaluate trade-offs.

Responsibilities

• Build the AI core of the platform.
• Design and ship LLM-powered features and autonomous agents that author tests, diagnose failures, find root causes and repair broken tests.
• Build RAG and retrieval systems over test artifacts, logs, traces, DOM snapshots and code.
• Own chunking, embeddings, hybrid search, ranking and context construction; measure retrieval quality.
• Make AI production-grade.
• Set technical direction through design docs and prototypes.

Skills

Backend systems

Java or Go

LLMs / AI features

React frontend

Distributed systems

Tools

Docker

Kubernetes

Ansible

PostgreSQL

ClickHouse

OpenSearch/Elasticsearch

Redis

React

Job description

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.

About Element34

For years, Element34 has run large-scale test execution for enterprises that can't send their testing to a public cloud. Banks, insurers, healthcare companies and other regulated organizations use our platform, SBOX, to run thousands of Selenium, Playwright and Appium sessions in parallel on their own infrastructure. Now we're building an AI-native platform for the whole end-to-end test lifecycle: authoring, execution, failure analysis, triage and self-healing maintenance, with LLMs and agents at the core of each stage rather than added on afterward. We're a small, senior engineering team and we plan to stay that way. We have no layers of management, no handoffs between teams and no separate infra group. Each engineer owns a problem from design through production.

The Role

This is a hands-on Staff Engineer role with no direct reports. Your influence comes from the systems you design, the code you write and how you raise the engineering bar for the people around you. About 90% of your time will be backend work in Java or Go: distributed systems, concurrency, data pipelines, AI and agent infrastructure, and performance work. The other 10% is frontend work in React. This is not CRUD or API plumbing. You'll work on orchestration, scheduling, high-throughput ingestion, retrieval quality, agent reliability and performance problems that don't have a ready-made answer.

What You'll Do

• Build the AI core of the platform.
• Design and ship LLM-powered features and autonomous agents that author tests, diagnose failures, find root causes and repair broken tests.
• Build RAG and retrieval systems over test artifacts, logs, traces, DOM snapshots and code.
• You'll own chunking, embeddings, hybrid search, ranking and context construction, and measure retrieval quality rigorously.
• Make AI production-grade.
• Build evaluation harnesses, guardrails, cost and latency controls, and fallbacks so agents behave predictably in enterprise environments, including self-hosted and air-gapped ones.
• Design and build core platform systems in Java or Go, including session orchestration, scheduling, resource management and high-concurrency services behind thousands of parallel browser and device sessions.
• Own the data layer across PostgreSQL (transactional), ClickHouse (analytics and high-volume telemetry), OpenSearch (logs, search and vector retrieval) and Redis (caching, coordination, queues).
• Own delivery across the whole lifecycle.
• We have no separate infra team.
• You'll containerize with Docker, deploy and run on Kubernetes, automate with Ansible, and make sure the software runs reliably in customer environments you don't control.
• Solve hard problems end to end. Find the bottleneck, profile it, design the fix, ship it and watch it in production.
• Ship features in React when the work needs it, with no frontend team to hand off to.
• Set technical direction through design docs, architecture reviews and prototypes.
• Make the hard trade-off calls and explain them clearly.
• Make the team AI-native.
• Use AI coding tools every day and help shape how the team builds with them.

What We're Looking For

• Strong professional software engineering experience, with a strong record of building and owning complex backend systems.
• Deep expertise in Java or Go, including concurrency, memory management, performance tuning and idiomatic modern code. You write code others learn from.
• Mandatory: hands-on experience building with LLMs, including: RAG systems in production or close to it: embeddings, vector or hybrid search, chunking strategies, reranking and retrieval evaluation.
• AI agents: tool and function calling, multi-step planning, memory and state, and handling failures and non-determinism. Experience with MCP or agent frameworks is a plus. Knowing when to skip a framework counts too.
• Prompt and context engineering, plus evaluation. You know how to measure whether an LLM feature actually works.
• Daily, effective use of AI coding tools (Claude Code, Cursor, Copilot or similar).
• Strong computer science fundamentals and problem-solving ability. You can reason about correctness, performance and distributed-systems trade-offs from first principles.
• Production experience with data stores. Solid PostgreSQL skills plus hands-on experience with at least one of ClickHouse, OpenSearch/Elasticsearch or Redis at scale.
• Hands-on with Docker, Kubernetes and Ansible. You've deployed, debugged and operated your own services.
• Working proficiency in React and modern frontend development.
• An owner's mindset. You do well with autonomy and ambiguity, and you'd rather fix a