Technical Generative AI Lead - AWS Platform

Job Title: Technical Generative AI Lead - AWS Platform Company: Mount Talent Consulting Experience: 8 - 13 Years Skills: AWS, Python, LLM, Generative AI, Native Cloud, MLOps, AIOps, Cloud Architect - AI/ML, DevOps, Observability Services, RAG Description: Description : Generative AI Tech Lead (AWS-Native)Experience : Total 8- 15 years overall experience, with 3- 5 years leading cloud and AI engineering teamsRole 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 design2. Orchestration using AWS Strands SDK3. Integration patterns (sync, async, event-driven)Establish standards for :- Prompt engineering- Agent workflows- Model lifecycle management- Review designs and code to ensure performance, scalability, and maintainabilityGenerative AI Solution Delivery- Lead hands-on development of : 1. GenAI services, agents, and APIs using Python2. Bedrock Agent Core SDKbased applicationsGuide teams on :- Prompt optimization and guardrails- Cost-efficient token usage- Latency and throughput optimization- Drive adoption of RAG, embeddings, and vector storage patterns where appropriateAWS-Native Cloud Engineering : - Design secure, scalable AWS architectures using : 1. AWS Lambda, ECS, EKS, EC22. S3, DynamoDB, Aurora, OpenSearch3. API Gateway / ALB- Define IAM, networking, and security patterns aligned with Zero Trust and least privilege- Ensure high availability, fault tolerance, and disaster recovery strategiesDevOps, CI/CD & Platform Engineering : - Define and enforce CI/CD standards for GenAI workloads using : 1. AWS CodePipeline / CodeBuild / CodeDeploy2. GitHub Actions / GitLab CI- Lead Infrastructure-as-Code initiatives using :1. AWS CDK / CloudFormation / Terraform2. Automate testing, deployment, rollback, and environment promotionObservability, Reliability & Operations : - Own production observability strategy across AI and application layers : 1. CloudWatch logs, metrics, dashboards2. AWS X-Ray distributed tracing3. Custom metrics for AI behavior, latency, cost, and accuracy- Define and monitor SLAs, SLOs, and error budgets- Lead incident response, RCA, and continuous improvementSecurity, Governance & Responsible AI :- Ensure secure and compliant GenAI implementations : 1. Data encryption (at rest/in transit)2. Secrets management3. Secure prompt and data handling- Define guardrails for : 1. Data privacy2. Prompt injection risks3. Model misuse and hallucinations- Align AI implementations with enterprise governance and compliance frameworksTeam Leadership & Stakeholder Management : - Mentor and guide developers and senior engineers- Conduct design reviews, code reviews, and technical workshops- Collaborate with : 1. Product managers2. Security and compliance teams3. Platform and data engineering teams- Translate business requirements into scalable technical solutionsRequired Skills & Qualifications : Core Technical Skills (Must Have) : - Expert-level Python development- Strong hands-on experience with : 1. AWS Bedrock2. AWS Bedrock Agent Core SDK3. AWS Strands SDK- Deep expertise in AWS cloud-native architecture- CI/CD, DevOps automation, and Infrastructure as Code- Strong observability and production operations experiencePreferred Skills (Nice to Have) : Experience with : 1. RAG architectures2. Vector databases (OpenSearch, Pinecone, FAISS, etc.)3. Container platforms : Docker, Kubernetes (EKS)4. MLOps / Model lifecycle governance experience5. Cost optimization for large-scale AI workloads- Familiarity with Responsible AI frameworksLeadership & 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 .