What are the responsibilities and job description for the Artificial Intelligence/AI Engineer (AWS/Agentic AI/GenAI) position at InSource, Inc?
Artificial Intelligence/AI Engineer (AWS/Agentic AI/GenAI)
*Visa sponsorship is NOT available, so NO visa candidates please – NO 3rd party vendors reachout – Direct W2 candidates ONLY*
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
Please send resume to: aghosh@copiastaffing.com
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