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Niche senior ML/LLM leadership role with specific Bedrock and knowledge-graph requirements reduces competition.
Specialized agentic LLM, Bedrock, knowledge-graph and MLOps requirements limit transferability across industries.
Explicit 8+ years and 3+ years ML leadership plus mandatory Bedrock/LLM/knowledge-graph/MLOps skills.
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Lead architecture and implementation of advanced AI systems including agentic AI, LLM-driven applications, RAG, and enterprise Knowledge Graph within a scalable cloud-native environment.
Drive adoption and governance of AWS Bedrock and related AI/ML platforms for enterprise-scale, production-ready AI solutions.
Own ML engineering and MLOps practices, including pipeline automation, model lifecycle management, and cross-team data integrations to ensure AI-grade quality and compliance.
8+ years of software engineering experience with at least 3 years leading ML/AI initiatives in production.
Hands-on proficiency with full-stack development using Angular + TypeScript front-end and Python (Flask/FastAPI) back-end, deployed on AWS (ECR/ECS, CloudFront/S3).
Expertise with LLMs, embeddings, RAG, vector search, agentic AI development, and AWS services including Bedrock, SageMaker, Lambda, Step Functions, OpenSearch, DynamoDB.
Bachelor’s or Master’s degree in Computer Science, Data Science or related field.
Experienced in architecting and operationalizing autonomous, multi-step reasoning AI systems integrated with enterprise data and workflows.
Practiced in cloud-native AI/ML delivery with strong MLOps and infrastructure-as-code expertise (Terraform, Docker, Kubernetes).
Capable of cross-functional collaboration influencing AI platform strategy and mentoring engineers on cutting-edge ML and AI engineering practices.