Lead Data Engineer – AI/Machine Learning

Lead AI/Machine Learning Data Engineering role partnering with the VP, Head of Data to drive AI/ML enablement and readiness across the organization.

Responsibilities

• Design, build, and optimize data pipelines, ingestion frameworks, and platform components for analytics, reporting, and AI/ML use cases.
• Operate with autonomous ownership of complex initiatives, owning technical design through implementation and rollout with minimal oversight.
• Identify and resolve performance, scalability, and reliability issues across the existing data platform.
• Propose improvements proactively by recognizing gaps in data engineering and platform capabilities.
• Produce clean, well-tested, well-documented code and infrastructure-as-code while maintaining strong engineering hygiene.
• Help define AI/ML frameworks by evaluating and recommending tools, platforms, and standards for building and deploying AI/ML solutions.
• Build working AI/ML prototypes that deliver immediate value to engineering teams.
• Shape and support implementation of MLOps strategy, including model deployment, monitoring, versioning, and lifecycle management.
• Collaborate with Data Governance to ensure AI/ML frameworks align with data governance, security, and compliance requirements.
• Design and advocate for scalable AI/ML infrastructure patterns such as feature stores, curated/governed datasets, and streaming access for training and inference.
• Partner with Data Science, Data Engineering, and business stakeholders to assess AI/ML readiness gaps and build a roadmap to close them.
• Serve as a subject-matter expert and thought partner to the VP, Head of Data on emerging AI/ML technologies, practices, and industry trends.
• Document AI/ML standards, frameworks, and decisions to support consistent adoption as practices mature.
• Act as a senior technical resource for architecture guidance, design patterns, and best practices for AI readiness and ML Ops frameworks.
• Partner with Enterprise Architecture on establishing architectural blueprints for AI readiness.
• Other Duties as Assigned.

Requirements

• Data engineering fundamentals : deep expertise in data pipeline design, optimization, and distributed data processing (Spark, dbt, Airflow, Kafka, or equivalent).
• Data platforms : hands‑on experience with Snowflake , Databricks , and/or Azure Synapse Analytics , with the ability to architect and optimize workloads.
• Cloud : strong knowledge of AWS , Azure , or GCP , plus modern data warehouse/lakehouse architectures.
• Python : strong Python skills and production‑grade software engineering practices (testing, version control, code review) for shipping systems beyond notebooks.
• API and integration : experience building systems around models via orchestration, tool‑calling, and retrieval systems.
• LLM experience : practical experience with LLM APIs (e.g., OpenAI) and open‑weight models.
• Prompt engineering : prompt engineering and evaluation as an established discipline.
• Model fundamentals : understanding of context windows, tokenization, embeddings, and limitations such as hallucination, latency, and cost tradeoffs.
• Vector search : experience with vector databases (Pinecone, Weaviate, pgvector, etc.) and embedding models.
• Search techniques : chunking strategies, hybrid search, and reranking.
• Agent and orchestration : frameworks such as LangChain, LangGraph, LlamaIndex, or custom orchestration.
• Tool use / agents : function‑calling design, multi‑step reasoning chains, and agent memory/state management.
• Model adaptation : practical understanding of when to fine‑tune vs. prompt vs. RAG.
• Parameter‑efficient methods : familiarity with LoRA and related approaches.
• MLOps / LLMOps : experience with MLOps/LLMOps practices.
• Evaluation and observability : model evaluation frameworks, A/B testing for model outputs, and observability (tracing, logging model calls).
• Deployment patterns : latency/cost optimization, caching, streaming responses, and fallback handling.
• Versioning : versioning prompts and models, not only code.
• Safety and governance : safety, evaluation, governance awareness; bias/safety evaluation and appropriate handling of PII.

Technologies

• Spark, dbt, Airflow, Kafka
• Snowflake, Databricks, Azure Synapse Analytics
• AWS, Azure, GCP
• Python, OpenAI
• Pinecone, Weaviate, pgvector
• LangChain, LangGraph, LlamaIndex
• LoRA
• MLOps, LLMOps
• Data Vault 2.0, Ensemble data modeling techniques

Experience

• Minimum 7+ years in data engineering with experience on large‑scale, mature data platforms.
• 3+ years developing ML or AI deliverables, including deployment to production.
• Bachelor's degree in a related field or demonstrated equivalent experience in a related field required.
• Working knowledge of agentic workflows for engineering and architecture.
• Demonstrated autonomous technical ownership of complex projects from design through delivery with minimal oversight.
• Experience shaping AI/ML enablement (defining frameworks, evaluating MLOps tooling, or building infrastructure for training and deployment).
• Experience partnering with Data Governance, Data Science, or Compliance to align technical practices with governance and regulatory requirements.
• Track record of proposing and driving innovative technical solutions rather than only executing predefined plans.
• Experience designing or implementing agentic workflows for data engineering preferred.
• Experience working with Property & Casualty insurance carriers preferred.
• Experience with Data Vault 2.0 or Ensemble data modeling techniques preferred.

Location

• Cincinnati, OH (hybrid)

Benefits

• Medical, dental, vision, and life insurances
• Short and long‑term disability
• Company‑match of 100% of a 6% contribution 401(k) plan
• Employee Assistance Plan
• Health Savings Account
• Flexible Spending Account
• Health Reimbursement Account
• Wellness program
• Opportunities for professional development and advancement