AWS AI Engineer

Hudson Manpower seeks an accomplished AWS AI Engineer to design and deploy AI and ML solutions on AWS. The role centers on building scalable AI applications, including retrieval-augmented generation systems, fine-tuning large language models, and developing AWS-native microservices to support enterprise automation and governance. The position is based in Harrisburg, PA with remote work options.

Responsibilities

• Engage hands-on with AWS services such as Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, and S3 to architect and implement AI capabilities.
• Lead the deployment of AWS cloud services spanning infrastructure, machine learning, and AI platform services for enterprise scale automation and governance.
• Develop LLM driven applications, including Retrieval-Augmented Generation using LangChain and related frameworks.
• Build cloud-native microservices, APIs, and serverless functions to enable 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, a GitHub based SDLC, and infrastructure as code with Terraform.
• Support the development and evolution of reusable platform components for AI and ML operations.
• Create and maintain technical documentation for the team and internal customers.
• Exhibit strong English communication skills, both spoken and written.

Requirements

• Seven years of hands-on software engineering experience with a strong focus on Python.
• Proficiency with AWS services, particularly Bedrock or SageMaker.
• Experience fine-tuning large language models or building datasets and deploying ML models to production.
• Demonstrated experience with AWS Organizations and policy guardrails such as SCPs and AWS Config.
• Solid background in implementing Retrieval-Augmented Generation architectures and LangChain.
• Experience implementing Infrastructure as Code best practices and building Terraform modules for AWS.
• Strong Git-based version control, code reviews, and DevOps workflows.
• Proven ability to deliver production-ready software with release pipeline integration.
• AWS or relevant cloud certifications.
• Policy as Code development experience, for example Terraform Sentinel.
• Experience with Hugging Face, Golang, or Node.js.
• Exposure to FinOps and cloud cost optimization.
• Data science background or experience working with structured or unstructured data.
• Awareness of data privacy and compliance best practices such as PII handling and secure model deployment.

Technologies

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

Benefits

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

Top Skills

Must Have

• AWS services including Bedrock, SageMaker, ECS, and Lambda
• Experience with AWS Organizations and policy guardrails (SCPs, AWS Config)
• Proficiency in implementing RAG architectures and ML tooling such as Transformers, PyTorch, TensorFlow, and LangChain
• Infrastructure as Code best practices and building Terraform modules for AWS
• Fine-tuning large language models, building datasets, and deploying ML models to production
• Git-based version control, code reviews, and DevOps workflows
• AWS or relevant cloud certifications
• Policy as Code development (Terraform Sentinel)
• Experience with Hugging Face, Node.js, or Golang
• Exposure to FinOps and cloud cost optimization
• Data science background or experience with structured or unstructured data
• Awareness of data privacy and compliance best practices (PII handling, secure model deployment)

Nice To Have

• Additional cloud certifications or advanced AWS capabilities
• Further experience with data privacy and compliance programs
• Deeper background in data science across diverse data types
• Continued exposure to FinOps practices and cost optimization strategies
• Experience with additional ML frameworks or libraries beyond those listed
• Policy as Code development beyond Terraform Sentinel