Senior AI Engineer – Agentic Platform

Amtechsoftware · Bangalore

Location
Bangalore
Funding
N/A
Posted
Sep 1, 2026

Amtechsoftware is hiring a Senior AI Engineer – Agentic Platform based in Bangalore. Every apply link on Engg.space goes straight to the company's own careers page - no recruiter middleman, no generic job-board form.

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Role details

About Vista Equity Partners Vista Equity Partners is a leading global investment firm focused exclusively on enterprise software, data, and technology-enabled businesses. With over $100B in assets under management and a portfolio of 90+ software product companies worldwide, Vista accelerates growth through operational excellence, shared expertise, and long-term partnership. In India, Vista’s presence continues to expand with 45+ portfolio companies employing more than 17,000 professionals across technology, product, customer success, and operations — reinforcing India’s strategic role as a hub of innovation and talent within the Vista ecosystem. Through its Agentic AI Factory, Vista is embedding Generative AI across its global portfolio — enabling companies to integrate intelligent, responsible AI into products, operations, and decision-making. This initiative is strengthened through portfolio-wide learning programs, leadership workshops, and AI hackathons that foster innovation, build fluency, and accelerate practical AI adoption across teams. About Amtech Amtech LLC is a leading provider of enterprise software solutions for the packaging and manufacturing industries, helping businesses streamline operations, improve productivity, and drive sustainable growth. With a strong focus on innovation and customer success, Amtech continues to expand its global footprint and deliver technology that empowers teams around the world. Our Employee Value Proposition At Amtech, our people are our greatest differentiator. We create an environment where you can: Purpose Shape the future of manufacturing and supply chain operations by delivering mission-critical enterprise software used by industry-leading organizations. Growth Access continuous learning, leadership development, and cross-portfolio opportunities through Vista’s global network — accelerating both technical and managerial career paths. Culture Work in a collaborative, transparent, and people-first environment where values, accountability, and integrity guide every decision. Innovation Engage with cutting-edge technologies, including AI-driven automation, and contribute to modernizing financial systems and operational processes across the business. Role Description Amtech LLC is seeking a Senior AI Engineer to build and scale the Agentic Platform behind Amtech AI Intelligence — a headless, MCP-native intelligence layer across packaging and labels, and data that is callable by any AI, on any screen. This role takes Amtech's first production agent pattern and turns it into a reusable platform component that extends cleanly to EnCore, LabelTraxx, Siteline, Scorekeeper, and future use cases, rather than building a one-off solution per product. This is a senior, largely self-directed role: you'll set technical direction for the platform, make build-vs-buy calls on agent tooling, and be a technical reference point for the Forward Deployed Engineers embedding it into product pods. Curiosity about emerging agentic AI and LLM tooling, and comfort operating close to production systems and governed customer data, are essential. Job Description Headless, MCP-Native Architecture Build business logic that runs independent of any screen — the same capability must work for a person in the UI, an AI agent, or an automated job. Expose Amtech capabilities as MCP-described tools so any AI model can call them. Orchestration and Agent Execution Build and maintain the LLM orchestration layer that turns model output into safe, governed, tool-using actions. Implement and extend agent workflows using frameworks such as LangChain/LangGraph and the Model Context Protocol (MCP). Retrieval and Model Integration Build and tune RAG pipelines for grounding agent responses in Amtech's own data. Integrate model-agnostic LLM providers — Claude, OpenAI, Azure OpenAI, AWS Bedrock, and whatever model leads next — via a common provider abstraction, avoiding vendor lock-in. Governed On-Prem Data Access (the Data Agent) Build and maintain the Data Agent — new technology that reads EnCore and other customer data directly for inferencing, with no dependency on legacy point-to-point APIs. Ensure every agent action passes governed approval gates before it executes, with a complete, queryable audit trail — customer data stays in the customer's environment while still enabling cloud-LLM reasoning over it. Building Amtech AI Intelligence Capabilities Extend the platform into concrete delivered capabilities such as Schedule Intelligence (watching schedules, flagging conflicts and trim-loss, proposing re-sequencing), Order & Customer Intelligence (full-context status across specs, history, and dates), Docs & Knowledge (cited, grounded answers with stale-document detection), and Forecast & KPI Intelligence (plain-language questions over throughput, waste, and OTIF). Plugging Into Amtech's Broader Agent Portfolio Amtech already runs 10+ active agents company-wide — this role's platform work is what lets patterns from those efforts be reused instead of rebuilt. Directly support the Utilization mandate to extend engineering's agent-building cadence to Sales, Support, and Customer Success, so new agents in those functions can be built on the same governed orchestration, RAG, and audit foundation rather than one-off tooling. Scaling the Platform Pattern Across Product Lines Turn Amtech's first production agent pattern into a reusable platform component that other product pods can adopt without a rebuild. Partner with Forward Deployed Engineers embedded in product pods to validate the platform against real workflows and feed field learnings back into the roadmap. Deployment, Monitoring, and Cost Control Deploy agents and inference endpoints on AWS (e.g., Lambda, ECS/EKS, SageMaker) and integrate them into existing APIs and microservices. Set up evals, cost controls, and basic observability to catch regressions, drift, or runaway spend before they reach customers. Bridging into ML Engineering and M

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