AI/ML Engineer (AWS Certified)

About the Role

We're looking for a hands-on AWS AI Engineer to design, build, and deploy AI solutions that solve
real business problems. You'll work at the intersection of cloud infrastructure and modern AI,
turning foundation models and AWS-native services into production-grade applications. This is a
high-impact role with significant ownership — you'll collaborate closely with product and DevSecOps
teams to move prototypes into scalable, reliable systems.
● Design & Build AI Architectures: Develop end-to-end AI solutions selecting the right
paradigm for the problem, spanning classical ML, NLP, and GenAI.
● Own Generative AI & Agentic Workflows: Design and deploy multi-agent systems and RAG
architectures using Amazon Bedrock, AgentCore, and Strands Agents (or open-source
equivalents like LangGraph/CrewAI), integrating foundation models like Claude and Nova.
● Manage Traditional ML Lifecycle: Build, tune, and deploy classical ML and computer vision
models on AWS SageMaker (Pipelines, batch/real-time endpoints) alongside managed AWS
AI services (Rekognition, Textract etc.).
● Champion Responsible AI & Observability: Implement end-to-end evaluation, guardrails,
and bias detection using Bedrock Guardrails and AgentCore Policy. Monitor model drift and
system traces using AgentCore Observability, CloudWatch, and OpenTelemetry.
● Ship Reliable Software & MLOps: Write clean, async Python and Infrastructure-as-Code
(AWS CDK or Terraform) to automate CI/CD pipelines, collaborating closely with DevSecOps
and the product team to deploy private, production-grade applications.
Requirements (Must Have)
● 3–4 years in software/data engineering with at least 2+ years specialized in AI/ML, including
at least one GenAI or agentic system shipped to production.
● Strong proficiency in Python; highly comfortable with async programming, decorators, and
API-driven patterns.
● Hands-on experience with the AWS AI ecosystem.
● Solid grasp of LLM fundamentals: context windows, tool use/function calling, RAG vs.
fine-tuning, and evaluation frameworks.
● Experience building with at least one agentic framework (e.g., Strands Agents, LangGraph,
CrewAI).
● Familiarity with vector search and embedding pipelines (OpenSearch, pgvector, etc.).
● Comfort with Infrastructure-as-Code (AWS CDK or Terraform).
● Valid AWS Associate Level certification (e.g., Solutions Architect Associate or Machine
Learning Engineer Associate).
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