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

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

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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

• Strong hands-on Python development experience.
• Experience developing production AI/ML applications.
• 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.
• AWS cloud experience.
• Experience integrating APIs, databases, or enterprise applications into AI solutions.

Highly Preferred

• Dataiku
• AWS Bedrock / AgentCore
• Model Context Protocol (MCP)
• 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