Senior AI Engineer – Agentic Platform

📋 Job Overview

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.

🏢 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.

🎯 The Role

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.

✅ Key Responsibilities

• 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.
• 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).
• 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.
• 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.
• 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).
• 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.
• 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.
• 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.
• Bring enough classical ML engineering and MLOps fluency (feature stores, model registries, CI/CD for models, drift monitoring) to partner effectively with the Data Science, ML Engineering, and MLOps functions, not just the agentic/LLM side of the platform.

📌 Required Qualifications

• Bachelor's Degree in Computer Science, Software Engineering, Data Science, or related field.
• 6+ years of software engineering experience, including 2+ years hands-on building production systems with large language models.
• Demonstrated experience setting technical direction and reviewing the work of other engineers, even if not a formal people-management role.
• Strong Python programming skills.
• Practical experience with prompt engineering, tool/function calling, and agent frameworks (e.g., LangChain, LangGraph).
• Experience integrating at least one major LLM provider API (OpenAI, Anthropic, Azure OpenAI, or AWS Bedrock).
• Hands-on AWS experience (e.g., Lambda, ECS/EKS, S3, IAM, CloudWatch) deploying and operating production workloads in the cloud.
• Experience with model inferencing — real-time and batch — including optimizing latency, throughput, and cost for LLM or ML workloads.
• Experience building or consuming RAG pipelines and working with vector databases.
• Working knowledge of classical ML engineering and MLOps practices (e.g., feature stores, model registries, MLflow/Kubeflow-style CI/CD for models) — enough to partner with Data Science, ML Engineering, and MLOps, not necessarily hands-on ownership.
• Familiarity with Docker and containerized deployment.
• Working knowledge of Git and CI/CD practices.

⭐ Desirable Experience

• Experience with the Model Context Protocol (MCP) or similar agent-tool integration standards.
• Experience building governed, read-only data access layers (audit logging, guardrails, least-privilege access).
• Exposure to Kubernetes and cloud AI/inferencing platforms (AWS Bedrock, AWS SageMaker); AWS certification is a plus, not a requirement.
• Experience working with ERP, MES, or other industrial/manufacturing data environments.
• Prior experience embedding directly with a product team or customer-facing engineering effort.
• Familiarity with performance monitoring tools (e.g., Prometheus, Grafana, Datadog).

🎁 Benefits

At Amtech, you will drive meaningful financial impact in a growing enterprise software organization while benefiting from Vista’s world-class ecosystem. You’ll collaborate with talented peers, leverage cross-portfolio learning programs, and help shape the future of Amtech’s financial operations and systems. Build your career with Amtech — backed by the strength, scale, and innovation culture of Vista.