AVP, AI & ML Engineering, Tech Lead

Key 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 behaviors
• 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

Qualification & Requirements

We’re looking for strong collaborators who deliver exceptional client experiences and thrive in fast-paced, team-oriented environments. Our ideal candidates pursue greatness, act with integrity, and are driven to help our clients succeed. We value those who embrace creativity, continuous improvement, and contribute to a culture where we win together and create and share joy in our work.

• 8+ years of software engineering experience, with 3+ years in hands-on leadership of ML/AI initiatives
• Proven leadership in designing and deploying machine learning systems in production
• Hands-on expertise with LLMs, embeddings, RAG, vector search, semantic retrieval, or agentic AI development
• Strong experience with AWS services, especially Bedrock, SageMaker, Lambda, Step Functions, OpenSearch, DynamoDB, ECR, and S3
• Strong 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

• Leadership & Mentorship
  • Provide architectural and engineering leadership across AI/ML Engineering
  • Mentor engineers on ML system design, LLM development, agentic architectures, and cloud-native AI delivery
  • Champion engineering excellence, innovation, and a builder mindset across the team
• Strategic Influence
  • Shape LPL’s enterprise AI roadmap, influencing priorities and platform capabilities
  • Translate complex AI concepts into executive-ready narratives
  • Evaluate emerging agentic technologies and generative AI frameworks to guide platform evolution

Benefits

No information available.