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Job Description

Hudson Manpower seeks a Senior AI AWS Engineer to design and deploy AI and ML applications on AWS, with a focus on retrieval-augmented generation (RAG), fine-tuning large language models, and AWS-native microservices for enterprise automation, insights, and governance. This role is based in Pittsburgh, PA with remote work options.

Responsibilities

  • Contribute hands-on with AWS services such as Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, and S3 to build and operate AI solutions.
  • Lead the implementation of AWS cloud services spanning infrastructure, machine learning, and AI platform capabilities.
  • Develop LLM based applications including Retrieval-Augmented Generation using LangChain and related frameworks.
  • Create cloud-native microservices, APIs, and serverless functions to support intelligent automation and real-time data processing.
  • Collaborate with internal stakeholders to translate business goals into secure, scalable AI systems.
  • Own the software release lifecycle, including CI/CD pipelines, GitHub based SDLC, and infrastructure as code with Terraform.
  • Support the evolution of reusable platform components for AI/ML operations.
  • Produce and maintain technical documentation for the team and internal customers.
  • Demonstrate excellent verbal and written communication skills in English.

Requirements

  • Seven years of hands-on software engineering experience with a strong emphasis on Python.
  • Experience with AWS services, particularly Bedrock or SageMaker.
  • Familiarity with fine-tuning large language models or building datasets and deploying ML models to production.
  • Proven experience with AWS Organizations and policy guardrails such as SCPs and AWS Config.
  • Solid track record implementing RAG architectures and LangChain.
  • Strong background in Infrastructure as Code and building Terraform modules for AWS.
  • Proficiency in Git-based version control, code reviews, and DevOps workflows.
  • History of delivering production-ready software with integrated release pipelines.

Technologies

  • Python
  • AWS Bedrock
  • AWS SageMaker
  • AWS Lambda
  • AWS Step Functions
  • AWS DynamoDB
  • AWS S3
  • LangChain
  • Transformers
  • PyTorch
  • TensorFlow
  • Terraform
  • Terraform Sentinel
  • Node.js
  • Hugging Face
  • Golang
  • Git
  • GitHub

Benefits

  • Competitive base salary
  • Medical, dental, and vision insurance coverage
  • Optional life and disability insurance provided
  • 401(k) with company match and optional profit sharing
  • Paid vacation time
  • Paid bench time
  • Training allowance offering
  • Eligibility for referral bonuses

General Function

We are hiring a Senior AI AWS Engineer who has actually built AI and ML applications in cloud environments, not merely studied them. The role centers on hands-on development of retrieval-augmented generation systems, fine-tuning LLMs, and AWS-native microservices that drive automation, insight, and governance in an enterprise setting. You will design and deliver scalable, secure services that bring large language models into real operational use by connecting them to live infrastructure data, internal documentation, and system telemetry. You will join a high-impact team advancing cloud-native AI in a real-world enterprise environment.

This is a builder role within our Public Cloud AWS Engineering team. It is not focused on buzzwords or theory, but on architecting, developing, and supporting production AI/ML services with real data and users. If you have hands-on experience delivering tangible AI solutions in production, this is the right opportunity.

Supervisory Responsibilities

None

Nice to Haves

  • AWS or relevant cloud certifications
  • Policy as Code development experience, such as Terraform Sentinel
  • Experience with Hugging Face, Golang, or Node.js
  • Exposure to FinOps and cloud cost optimization
  • Data science background or experience with structured and unstructured data
  • Awareness of data privacy and compliance best practices, including PII handling and secure model deployment

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