AWS AI Engineer / USC and GC Candidates can ONLY Apply

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!