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 setting where people can do their best work. We build software for UK manufacturers who can't afford to get things wrong: aerospace, medical devices, oil and gas, motorsport. We're now building a new product with AI on every stage of that flow.
We're part of Banyan Software, the best permanent home for software businesses that serve specialised industries, their employees, and their customers. A manufacturer runs one flow: enquiry, quote, order, jobs, scheduling, tracking, quality, shipping, invoice. We're building the system of action on top of
it: AI that works on every stage, capturing enquiries, validating orders, optimising schedules, anticipating problems, and reconciling invoices, always with a human in control and every step
You'll be the company's first AI engineer. There's not much AI in production today: no agents, no runtime, no AI layer. It has two parts: the events that announce what just happened in the business, and the agents that act on them. NET services with architecture boundaries enforced in CI, and a growing catalog of transaction-safe business operations ready to become AI tools. This is a founding role: you set the architecture, the standards and the evaluation discipline that every AI feature in this product gets built on.
Build the agentic layer: one coherent layer that every AI capability in the product runs on. Once it exists, adding AI to any stage of the manufacturing flow is a few weeks of feature work.
The runtime and the tools Architect the agent runtime: a server-side loop with durable runs, retries, scheduled and event-driven triggers, and human-in-the-loop steps




Build the tool layer that turns our catalog of transaction-safe business operations into typed, tenant- scoped, permissioned AI tools, for our own agents and for the outside world through MCP
Evaluation and audit Make evals part of how we ship: fixture-based tests of every AI behaviour, wired into CI from day one Close the correction loop: every override, rejection and edit captured as structured signal, so evals and retrieval improve from real use Build agent auditing into the platform's tamper-evident action log as it lands: every agent step logged, attributable and replayable. Our customers pass AS9100 audits, and explainable AI is a
feature we sell Grounding and operations Drive the event catalog: which business facts the product announces, from the office to the shop floor (SalesOrderCreated, GoodsReceived, JobRunningLate). Every agent run starts from one Grow the semantic grounding: a machine-readable model of the business and how it fits together, so agents retrieve facts instead of inventing them
Run what you ship: your agents in production are yours, including the incident when one misbehaves

First six months: more agents are live on the same layer, and at least one of them was built by one of the full stack engineers.
First twelve months: adding AI to a stage of the flow is a few weeks of feature work for any engineer here, and you can show an auditor how any of it reached the answer it did.
Linear as the single board: work traced from issue to acceptance criteria to PR, reporting as a by- product of the work
A small team: full stack engineers with a lead, plus AI and platform specialists, on a .NET and Next.js platform on Azure Prototype-led: we design the flow before we build it,



against a shared design system
AI in daily use across the team: coding agents in the repo with the context they need, and AI tooling
A customer base we can pick up the phone to, and an implementation team on their sites every week
Tech Stack You'll Use
Backend: C#, .NET 10, Rebus messaging, SQL Server (database per company)
Frontend: Next.js, React, TypeScript
Cloud: Azure (Container Apps, Azure SQL, Service Bus, Front Door), Bicep, GitHub Actions
AI: Model-agnostic by design, with MCP as the tool protocol
You've taken multiple LLM-powered systems from prototype to production, at least one of them agentic (multi-step, tool-using, running unattended), and you can walk us through what broke and
what you'd redo Product instincts and a builder's disposition: you can sit with a planner or a quoting manager, translate their workflow into an agent architecture without being handed a spec, then write the code,
ship it, and talk to the people who use it Strong backend and systems engineering experience (5+ years). You treat evals and observability as part of the definition of done, and you've wired LLM evaluation and observability tooling
You treat AI security as an engineering problem: injection containment, retrieved content handled strictly as data, and hostile inputs in the eval suite
Comfortable being first: setting standards, writing them down, and reviewing others' work against them
Preferred Experience
You've shipped something in manufacturing, ERP, supply chain or industrial operations, or you're genuinely curious about how factories run
TechGrove may use artificial intelligence (AI) tools to assist in screening and/or assessing applicants during the recruitment process. Contact us at any time with questions about our process or to request accommodation.
We have been made aware of individuals fraudulently posing as members of our Talent Acquisition team and extending fake job offers.

📌 Tech / AI Specialist (Bengaluru)
🏢 TechGrove by Banyan Software
📍 Bengaluru