Artificial Intelligence/AI Engineer (AWS/Agentic AI/GenAI)

InSource, Inc; Reading (Berks County); Hybrid; USD 120,000 - 160,000; Full time

Job summary

InSource, Inc. is seeking an AI Engineer to design, build, and deploy production-ready AI solutions in a large enterprise environment. The role emphasizes agentic AI workflows and scalable ML on AWS, combining strong Python engineering with practical GenAI experience.

You will work across functions to operationalize AI, build reusable components, and ensure reliability, security, observability, and cost control in hybrid onsite settings in Reading, PA or Tampa, FL.

Qualifications

• Hands-on experience with Amazon SageMaker in production
• Experience with Generative AI, LLMs, RAG and agentic AI architectures
• Experience with agent orchestration frameworks (LangGraph, CrewAI, AutoGen)
• Strong ML engineering, MLOps, deployment, monitoring and lifecycle management
• Experience API/database integration into AI solutions

Responsibilities

• Design and develop AI-powered applications and agentic workflows using Python 3.11+.
• Build, train, deploy, and monitor ML models with SageMaker.
• Develop end-to-end ML workflows including feature engineering, deployment and monitoring.
• Create agentic AI solutions with LangGraph, CrewAI, AutoGen or equivalents.
• Work with LLMs, prompt engineering, and knowledge retrieval.
• Integrate AI agents with APIs, databases and enterprise systems.
• Leverage AWS Bedrock and AgentCore for enterprise AI.
• Use Dataiku for data prep and ML workflows.
• Collaborate with data scientists, architects and stakeholders to operationalize AI solutions.

Skills

SageMaker

Generative AI

LLMs

RAG

Agentic AI

AI orchestration

ML engineering

MLOps

API integration

Tools

AWS Bedrock

Dataiku

LangGraph

CrewAI

AutoGen

Docker

Kubernetes

Terraform

GitHub Actions

Job description

Artificial Intelligence/AI Engineer (AWS/Agentic AI/GenAI)

No current or future employment sponsorship available.

Reading, PA OR Tampa, FL | Hybrid (2-3 days onsite per week) | Contract-to-Hire (or Direct Hire, client is flexible)

One onsite interview is required | Local candidates to work onsite & appear for onsite interview

Reading, PA OR Tampa, FL | Hybrid (2-3 days onsite per week) | Contract-to-Hire (or Direct Hire, client is flexible)

One onsite interview is required | Local candidates to work onsite & appear for onsite interview

Summary

We are seeking an experienced AI Engineer to design, build, and deploy production-ready AI and machine learning solutions in a large enterprise environment.

This is a hands-on engineering role focused on building intelligent applications, agentic AI workflows, and scalable ML solutions on AWS. The ideal candidate combines strong Python/software engineering skills with practical experience in Generative AI, machine learning, and cloud-based AI platforms.

Responsibilities

• Design and develop AI-powered applications and agentic workflows using Python 3.11+.
• Build, train, deploy, and monitor ML models using Amazon SageMaker.
• Develop end-to-end ML workflows covering feature engineering, deployment, monitoring, and model lifecycle management.
• Build agentic and multi-agent AI solutions using LangGraph, CrewAI, AutoGen, or similar frameworks.
• Work with LLMs, RAG architectures, prompt engineering, and knowledge retrieval.
• Integrate AI agents with APIs, enterprise systems, databases, and external tools.
• Utilize AWS Bedrock and emerging AgentCore capabilities for enterprise agentic AI.
• Use Dataiku for data preparation, analytics, feature engineering, and ML workflows.
• Partner with data scientists, architects, engineering teams, and business stakeholders to operationalize AI solutions.
• Develop reusable AI components and deployment patterns with an emphasis on scalability, reliability, security, observability, and cost.

Qualifications

• Hands-on Amazon SageMaker experience.
• Experience with Generative AI, LLMs, RAG, and agentic AI architectures.
• Experience with agent orchestration frameworks such as LangGraph, CrewAI, AutoGen, or equivalent.
• Strong understanding of ML engineering, MLOps, model deployment, monitoring, and lifecycle management.
• Experience integrating APIs, databases, or enterprise applications into AI solutions.

Highly Preferred

• AWS Bedrock / AgentCore
• Vector databases and knowledge retrieval
• LangChain or LlamaIndex
• Docker, Kubernetes, Terraform, GitHub Actions, or CI/CD
• Experience building AI solutions in a large enterprise environment