AWS AI Engineer / USC and GC Candidates can ONLY Apply
At a glance
Mid-level AI Engineering role at Hudson Manpower. Remote, United States · full-time · no salary stated · we estimate $162,369–$300,000.
Hudson Manpower has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build RAG applications and fine-tuned language models that connect with infrastructure data, internal documents and system telemetry
- Develop AWS microservices, APIs and serverless functions for intelligent automation and live data processing
- Turn stakeholder goals into secure AI systems, then manage their releases through CI/CD, GitHub and Terraform
- Create reusable AI/ML platform components and document them for teammates and internal customers
What you bring
- At least seven years of hands-on software engineering, with Python as a major focus
- You have used AWS, particularly Bedrock or SageMaker, and delivered machine-learning models or datasets
- You have built RAG systems with LangChain and worked with AWS organization policies and guardrails
- You can create Terraform modules and have shipped production software through Git-based reviews and release pipelines
Who this fits
This suits a hands-on, mid-level builder with production experience in cloud AI and machine learning; the role has no supervisory duties. You must be authorized to work in the United States as a U.S. citizen or green card holder, and the position is remote in the USA.
From the employer
Job Title: AWS AI Engineer
Location: REMOTE USA
TOP SKILLS:
Must Have
AWS services- Bedrock, SageMaker, ECS and Lambda
Demonstrated experience with AWS organizations and policy guardrails (SCP, AWS Config)
Experience implementing RAG architectures and using frameworks and ML tooling like: Transformers, PyTorch, TensorFlow, and LangChain
Experience in Infrastructure as Code best practices and experience with building Terraform modules for AWS cloud
Fine-tuning large language models, building datasets and deploying ML models to production
Git-based version control, code reviews, and DevOps workflows
Nice To Have
AWS or relevant cloud certifications
Data privacy and compliance best practices (e.g., PII handling, secure model deployment)
Data science background or experience working with structured/unstructured data
Exposure to FinOps and cloud cost optimization
Hugging Face, Node.js
Policy as Code development (I.e. Terraform Sentinel)
What You’ll Do
GENERAL FUNCTION:
We are hiring a Sr AI AWS Engineer who has actually built AI/ML applications in cloud—not just read about them. This role centers on hands-on development of retrieval-augmented generation (RAG) systems, fine-tuning LLMs, and AWS-native microservices that drive automation, insight, and governance in an enterprise environment. You’ll design and deliver scalable, secure services that bring large language models into real operational use—connecting them to live infrastructure data, internal documentation, and system telemetry.
You’ll be part of a high-impact team pushing the boundaries of cloud-native AI in a real-world enterprise setting. This is not a prompt-engineering sandbox or a resume keyword trap. If you’ve merely dabbled in BedRock, mentioned RAG on LinkedIn, or read about vector search—this isn’t the right fit. We’re looking for candidates who have architected, developed, and supported AI/ML services in production environments.
This is a builder’s role within our Public Cloud AWS Engineering team. We aren’t hiring buzzword lists or conference attendees. If you’ve built something you’re proud of—especially if it involved real infrastructure, real data, and real users—we’d love to talk. If you’re still learning, that’s great too—but this isn’t an entry-level role or a theory-only position.
DUTIES AND RESPONSIBILITIES:
Hands-on role using AWS (Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, S3).
Responsible for the implementation of AWS cloud services including infrastructure, machine learning, and artificial intelligence platform services.
Experience with LLM-based applications, including Retrieval-Augmented Generation (RAG) using LangChain and other frameworks.
Develop cloud-native microservices, APIs, and serverless functions to support intelligent automation and real-time data processing.
Collaborate with internal stakeholders to understand business goals and translate them into secure, scalable AI systems.
Own the software release lifecycle, including CI/CD pipelines, GitHub-based SDLC, and infrastructure as code (Terraform).
Support the development and evolution of reusable platform components for AI/ML operations.
Create and maintain technical documentation for the team to reference and share with our internal customers.
Excellent verbal and written communication skills in English.
SUPERVISORY RESPONSIBILITIES: None
MINIMUM KNOWLEDGE, SKILLS, AND ABILITIES REQUIRED:
7 years of hands-on software engineering experience with a strong focus on Python.
Experienced with AWS services, especially Bedrock or SageMaker
Familiar with fine-tuning large language models or building datasets and/or deploying ML models to production.
Demonstrated experience with AWS organizations and policy guardrails (SCP, AWS Config).
Solid experience implementing RAG architectures and LangChain.
Demonstrated experience in Infrastructure as Code best practices and experience with building Terraform modules for AWS cloud.
Strong background in Git-based version control, code reviews, and DevOps workflows.
Demonstrated success delivering production-ready software with release pipeline integration.
Nice-to-Haves:
AWS or relevant cloud certifications.
Policy as Code development (e.g., 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/unstructured data.
Awareness of data privacy and compliance best practices (e.g., PII handling, secure model deployment).
What You’ll Get
Competitive base salary
Medical, dental, and vision insurance coverage
Optional life and disability insurance provided
401(k) with a company match and optional profit sharing
Paid vacation time
Paid Bench time
Training allowance offering
You’ll be eligible to earn referral bonuses!