Technical Generative AI Lead - AWS Platform

Description : Generative AI Tech Lead (AWS-Native)

Experience : Total 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 :

1. AWS Bedrock models and agent design

2. Orchestration using AWS Strands SDK

3. Integration patterns (sync, async, event-driven)

Establish standards for :

- Agent workflows

- Model lifecycle management

- Review designs and code to ensure performance, scalability, and maintainability

Generative AI Solution Delivery

- Lead hands-on development of :

1. GenAI services, agents, and APIs using Python

2. Bedrock Agent Core SDKbased applications

Guide teams on :

- 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 :

1. AWS Lambda, ECS, EKS, EC2

2. S3, DynamoDB, Aurora, OpenSearch

3. 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 :

1. AWS CodePipeline / CodeBuild / CodeDeploy

2. GitHub Actions / GitLab CI

- Lead Infrastructure-as-Code initiatives using :

1. AWS CDK / CloudFormation / Terraform

- Own production observability strategy across AI and application layers :

1. CloudWatch logs, metrics, dashboards

2. AWS X-Ray distributed tracing

3. 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 :

1. Data encryption (at rest/in transit)

2. Secrets management

3. Secure prompt and data handling

- Define guardrails for :

1. Data privacy

2. Prompt injection risks

3. 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 :

1. Product managers

2. Security and compliance teams

3. Platform and data engineering teams

- Translate business requirements into scalable technical solutions

Required Skills & Qualifications :

Core Technical Skills (Must Have) :

- Expert-level Python development

1. AWS Bedrock

2. AWS Bedrock Agent Core SDK

3. 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 :

1. RAG architectures

2. Vector databases (OpenSearch, Pinecone, FAISS, etc.)

3. Container platforms : Docker, Kubernetes (EKS)

4. MLOps / Model lifecycle governance experience

5. Cost optimization for large-scale AI workloads

- Familiarity with Responsible AI frameworks

Leadership & Soft Skills :

- 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