Job description

Lead GenAI Engineer

Location: Remote

Job Type: Full-time

Experience: 5–9 years

Cloud Requirement: AWS Mandatory (Amazon Bedrock & SageMaker)

About the Role We need a technical leader to own the architecture and deployment of our Generative AI systems entirely on AWS. This is a hands-on leadership role-you will be making the big architectural calls, mentoring a talented team, and personally building the most complex parts of our GenAI platform. Because our infrastructure is heavily cloud-native, deep and proven, experience with Amazon Bedrock and SageMaker is a hard requirement. If you enjoy owning a GenAI strategy end-to-end and scaling systems securely in the cloud, we want to talk to you.

What You'll Do

Architect and lead the development of end-to-end GenAI systems, covering everything from data ingestion to deployment and observability.
Build and scale solutions using Amazon Bedrock and SageMaker JumpStart, while integrating complementary services like OpenSearch Serverless.
Drive technical direction for complex RAG architectures (GraphRAG, agentic RAG) and multi-agent orchestration.
Lead and mentor a team of AI engineers and data scientists, growing their technical depth through design and code reviews.
Own the "FinOps" of our GenAI workloads-managing token economics, caching strategies, and provisioned throughput to keep costs in check.
Ensure enterprise-grade security and compliance by strictly applying AWS best practices (IAM least-privilege, KMS encryption, VPC isolation).

What We’re Looking For

5-9 years in software engineering, ML, or data science, including 1-2+ years operating in a lead or architectural role.
2+ years of hands-on experience actively shipping Generative AI products into production.
AWS Mastery: Extensive, hands-on experience provisioning and scaling models via Amazon Bedrock and SageMaker, alongside strong Infrastructure-as-Code skills (Terraform, CDK).
High-level proficiency in Python, the AWS SDK (boto3), and building asynchronous APIs (like FastAPI).
Expertise in agentic workflows (LangGraph, CrewAI) and prompt engineering at scale.
Excellent communication skills with the ability to translate technical trade-offs to executives and business stakeholders.

Bonus Points If You Have

Active AWS Certifications (e.g., AWS Certified Machine Learning - Specialty, or Solutions Architect).
Experience fine-tuning models or managing custom model deployments on Bedrock.
A background working in regulated industries (healthcare, finance) where navigating strict data privacy compliance is crucial.

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