AVP, AI & ML Engineering, Tech Lead

Responsibilities

• AI/ML Architecture & Agentic System Design: Architect and lead implementation of agentic AI systems capable of multi-step reasoning, tool use, workflow orchestration, and domain-specific autonomy. Build LLM-based agents that interact with APIs, data products, and enterprise systems to drive intelligent automation and decision support. Design orchestration layers that incorporate memory, context management, and dynamic planning for advanced agent behaviours. Develop RAG architectures that integrate embeddings, vector search, and semantic retrieval into agentic workflows
• AWS Bedrock–Driven AI Platform Engineering: Lead adoption of AWS Bedrock for model selection, orchestration, governance, and enterprise scaling of LLMs and generative AI. Implement Bedrock Features such as: Guardrails, Model evaluation, Provisioned throughput, Custom model fine-tuning. Integrate Bedrock models with downstream systems, including microservices, pipelines, and agent frameworks. Partner with enterprise architecture to define standards for Bedrock usage and model lifecycle governance.
• Knowledge Graph & Semantic Intelligence: Own engineering and operationalization of the Enterprise Knowledge Graph and integrate it with LLM and agent frameworks as a structured reasoning layer, Implement ontology-driven enrichment, entity resolution, and graph-based retrieval for AI capabilities, Connect graph services to agents to support semantic grounding, consistency, and contextual awareness
• ML Engineering & MLOps: Build and maintain automated pipelines for training, evaluation, deployment, and monitoring of traditional and generative ML models, Establish rigorous MLOps standards including CI/CD, reproducibility, drift detection, and quality gates, Develop feature stores, vector databases, and real-time inference services optimized for AI workloads.
• Engineering Delivery & Cross-Team Collaboration: Partner deeply with DIME to ensure upstream data pipelines meet enterprise AI-grade requirements for freshness, quality, lineage, and metadata, Work with Lakehouse Engineering and Data Product teams to align models, features, and data availability, Collaborate with governance and security teams to enforce responsible AI practices and regulatory controls

What are we looking for?

Requirements

• 8+ years of software engineering experience, with 3+ years in hands-on leadership of ML/AI initiatives. Should have leadership in designing and deploying machine learning systems in production.
• Full-stack proficiency building agentic/chat web experiences with Angular + TypeScript on the front end and Python (Flask/FastAPI) services on the back end. Experience implementing real-time/streaming UIs for LLM chat (e.g., server-sent events / streamed token responses) with responsive state management. Comfortable owning a feature end-to-end: RESTful API design, front-end integration, and deployment of both tiers to AWS (containerized services, ECR/ECS, CloudFront/S3-hosted SPA)
• Hands-on expertise with LLMs, embeddings, RAG, vector search, semantic retrieval, or agentic AI development. Should have experience with AWS services, especially Bedrock, SageMaker, Lambda, Step Functions, OpenSearch, DynamoDB, ECR, and S3
• Experience building/managing agentic solutions with solid understanding of agent orchestration, tool use, and guardrails for autonomous systems
• Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, Hugging Face). Familiarity with graph databases, ontologies, and knowledge graph technologies (Neptune, Neo4j, RDF/OWL)
• Excellent communication and cross-functional collaboration skills. Strong experience with MLOps and infrastructure-as-code (Terraform, Docker, Kubernetes). Bachelor’s or Master’s degree in Computer Science, Data Science or a related field (years experience may apply to cover this requirement)

Core Competencies