Gen AI Lead AWS Bedrock

Job Description

Generative AI Tech Lead (AWS-Native)

Experience

8 15 years overall experience with 3 5 years leading cloud and AI engineering teams

Role Summary

We are looking for a Generative AI Tech Lead to provide technical leadership and architectural direction for building and operating production-grade GenAI solutions on AWS. This role combines hands-on development solution architecture team mentorship and operational ownership.

The Tech Lead will own end-to-end delivery of GenAI platforms using Python AWS Bedrock (Agent Core SDK) AWS Strands SDK and modern DevOps and observability practices ensuring scalability security reliability and cost efficiency.

Key Responsibilities

Technical Leadership & Architecture

Define and own end-to-end architecture for Generative AI applications deployed natively on AWS

Lead technology decisions around: AWS Bedrock models and agent design

Orchestration using AWS Strands SDK

Integration patterns (sync async event-driven)

Establish standards for: Prompt engineering

Agent workflows

Model lifecycle management

Classification: Internal

Review designs and code to ensure performance scalability and maintainability

Generative AI Solution Delivery

Lead hands-on development of: GenAI services agents and APIs using Python

Bedrock Agent Core SDK based applications

Guide teams on: Prompt optimization and guardrails

Cost-efficient token usage

Latency and throughput optimization

Drive adoption of RAG embeddings and vector storage patterns where appropriate

AWS-Native Cloud Engineering

Design secure scalable AWS architectures using: AWS Lambda ECS EKS EC2

S3 DynamoDB Aurora OpenSearch

API Gateway / ALB

Define IAM networking and security patterns aligned with Zero Trust and least privilege

Ensure high availability fault tolerance and disaster recovery strategies

DevOps CI/CD & Platform Engineering

Define and enforce CI/CD standards for GenAI workloads using: AWS CodePipeline / CodeBuild / CodeDeploy

GitHub Actions / GitLab CI

Lead Infrastructure-as-Code initiatives using:

Classification: Internal

AWS CDK / CloudFormation / Terraform

Automate testing deployment rollback and environment promotion

Observability Reliability & Operations

Own production observability strategy across AI and application layers: CloudWatch logs metrics dashboards

AWS X-Ray distributed tracing

Custom metrics for AI behavior latency cost and accuracy

Define and monitor SLAs SLOs and error budgets

Lead incident response RCA and continuous improvement

Security Governance & Responsible AI

Ensure secure and compliant GenAI implementations: Data encryption (at rest/in transit)

Secrets management

Secure prompt and data handling

Define guardrails for: Data privacy

Prompt injection risks

Model misuse and hallucinations

Align AI implementations with enterprise governance and compliance frameworks

Team Leadership & Stakeholder Management

Mentor and guide developers and senior engineers

Conduct design reviews code reviews and technical workshops

Collaborate with:

Classification: Internal

Product managers

Security and compliance teams

Platform and data engineering teams

Translate business requirements into scalable technical solutions

Required Skills & Qualifications

Core Technical Skills (Must Have)

Expert-level Python development

Strong hands-on experience with: AWS Bedrock

AWS Bedrock Agent Core SDK

AWS Strands SDK

Deep expertise in AWS cloud-native architecture

CI/CD DevOps automation and Infrastructure as Code

Strong observability and production operations experience

Preferred Skills (Nice to Have)

Experience with: RAG architectures

Vector databases (OpenSearch Pinecone FAISS etc.)

Container platforms: Docker Kubernetes (EKS)

MLOps / Model lifecycle governance experience

Cost optimization for large-scale AI workloads

Familiarity with Responsible AI frameworks

Leadership & Soft Skills Classification: Internal

Strong architectural thinking and decision-making

Ability to coach and grow engineering talent

Excellent communication with technical and non-technical stakeholders

Ownership mindset for production systems