About the role

Role Overview

Koch Industries is seeking a GenAI / Agentic AI Engineer with 5-8 years of experience to design, build, and deploy autonomous AI agents that drive transformation across industrial operations, maintenance, and enterprise back-office workflows for subsidiaries such as GP and Molex.

This role focuses on delivering production-grade, scalable agentic AI solutions using AWS, AWS Bedrock, and the Databricks Lakehouse, integrated with enterprise platforms including SAP, and Salesforce. The emphasis is on measurable business impact, operational reliability, and governance-ready AI deployments

Role Summary

We are looking for an AI Engineer to lead the design and productionization of GenAI and agentic AI systems that autonomously orchestrate complex enterprise workflows. The role involves building intelligent agents capable of reasoning, planning, decision-making, and system interaction using LLMs, multi-agent architectures, and feedback-driven optimization techniques.

You will work at the intersection of AI engineering, cloud platforms, and business enablement, collaborating with users, product teams, and platform engineers to deliver framework-agnostic, cloud-native solutions aligned with AI governance, security, and compliance standards.

Key Responsibilities

· Design and implement autonomous AI agents using LLMs, planning algorithms, and decision-making frameworks

· Architect agent systems that support autonomy, interactivity, collaboration, and end-to-end task execution

· Integrate AI agents into enterprise applications, APIs, and workflows (e.g., copilots, chatbots, automation pipelines)

· Deploy and optimize Foundation Models (FMs) using AWS Bedrock, including prompt orchestration and guardrails

· Implement RAG architectures using Amazon OpenSearch or Kendra for enterprise knowledge access

· Leverage the Databricks Lakehouse for data preparation, feature engineering, evaluation, and feedback loops

· Collaborate with engineering, data, and product teams to iteratively enhance agent capabilities

· Optimize agent behavior using feedback loops, reinforcement learning concepts, and user interaction signals

· Monitor agent performance and implement observability, safety, and governance controls

· Maintain clear documentation covering architecture, agent logic, design decisions, and dependencies

Required Skills & Experience

· 5–8+ years of hands-on experience in AI, ML, or advanced data engineering, with direct exposure to GenAI and agentic AI systems

· Strong proficiency in Python, with experience building APIs using FastAPI / Flask and working with SQL-based data stores

· Proven experience deploying and optimizing Foundation Models (FMs) using AWS Bedrock, including Bedrock AgentCore for agent orchestration and tool execution

· Hands-on experience implementing Retrieval-Augmented Generation (RAG) patterns using Amazon OpenSearch or Amazon Kendra

· Demonstrated ability to build LLM-powered, agent-driven applications from use-case definition through production deployment on AWS

· Strong understanding of agentic AI concepts, including planning, reasoning, tool use, memory, and multi-agent collaboration

· Hands-on experience with agent frameworks such as Strands, LangChain, and CrewAI, including tool calling and workflow orchestration

· Experience working with the Databricks Lakehouse, including Delta tables, notebooks, workflows, and MLflow

· Familiarity with RAG architectures, vector databases, embeddings, and enterprise knowledge stores

· Experience building cloud-native APIs and backend services, supported by CI/CD pipelines

· Solid understanding of DevOps and infrastructure for GenAI workloads, including Docker, Kubernetes, and scalable inference architectures

Preferred / Good-to-Have Qualifications

· Experience with agent frameworks such as LangChain, LangGraph, AutoGen, or Model Context Protocol (MCP)

· Hands-on experience deploying autonomous agents using AWS-native services (Lambda, ECS/EKS, Step Functions)

· Familiarity with Amazon OpenSearch, Aurora, or DynamoDB for knowledge and state management

· Experience with prompt engineering, fine-tuning, and evaluation frameworks

· Strong understanding of reinforcement learning, planning algorithms, and multi-agent coordination

· Background in human–AI interaction, conversational UX, or simulation-driven systems

· Awareness of ethical AI, security, and governance best practices in regulated enterprise environments

· AWS certifications or experience operating in large-scale enterprise cloud environmentsRole Overview