AI ML Developer

Job Responsibilities - Identity Resolution

• Design and lead end-to-end identity resolution architecture combining probabilistic models ML and embedding-based techniques to build the authoritative customer identity graph
• Build and optimize large-scale entity matching systems across billions of records and multiple data domains - ensuring every US adult is accurately represented in CDP
• Architect advanced candidate generation and blocking strategies (LSH phonetic encoding semantic similarity) that balance precision with computational feasibility at population scale
• Design high-precision matching pipelines using ensemble approaches (rules ML LLM-based validation) to maximize accuracy of golden customer profiles
• Develop scalable clustering and graph-based approaches for unified customer identity resolution with clear confidence scoring and auditability
• Lead implementation of embedding pipelines and similarity search systems using transformer models for semantic-level identity matching

Job Responsibilities - AI/LLM

• Architect and build LLM-powered systems for entity resolution including zero-shot and few-shot classification workflows that handle edge cases traditional models miss
• Design and implement RAG-based architectures for enriching and contextualizing customer data from unstructured sources
• Lead development of NLQ-to-SQL platforms enabling business users to query CDP - the authoritative source of truth - using natural language
• Translate ambiguous business questions into structured queries with schema awareness semantic layers and guardrails that protect data integrity
• Define best practices for prompt engineering evaluation and LLM observability - ensuring AI outputs meet the trust standards CDP demands
• Design and optimize vector search architectures (Pinecone Qdrant pgvector) for large-scale retrieval across customer data
• Evaluate and integrate emerging frameworks such as LangChain LangGraph and agentic workflows where they strengthen CDP capabilities

Education and Work Experience

• Bachelors or Masters degree in Computer Science Data Science or related field
• 6 years of experience in ML/AI engineering
• Proven experience building production-grade entity resolution or identity graph systems at scale
• Experience designing LLM-based applications in enterprise environments with high accuracy and trust requirements

Technical Skills

• Advanced programming: Python
• Deep expertise in ML algorithms for similarity classification and clustering - particularly in identity resolution contexts
• Strong experience with transformer models embeddings and semantic search at population scale
• Hands-on experience with LLM APIs and orchestration frameworks
• Strong SQL and experience with distributed data processing (Spark Dask)
• Experience with vector databases and ANN search systems (FAISS Pinecone etc.)
• Expertise in ML lifecycle management (MLflow or equivalent)
• Understanding of data governance privacy and security requirements for customer identity data

Knowledge Skills and Abilities

• Strong system design and architectural thinking for AI/ML systems at population scale
• Ability to balance precision recall and scalability in identity resolution systems - understanding that accuracy directly impacts CDPs authority as the source of truth
• Strong understanding of data semantics and customer domain modeling across diverse data sources
• Leadership in driving AI engineering best practices standards and quality benchmarks
• Ability to collaborate across data engineering product security and business teams to deliver trusted customer intelligence