Lead, Data AI & Engineering

MHC•Burnsville, MN

2d•Remote

About The Position

MHC is seeking a hands-on Lead, Data & AI Engineering to build the data layer for the company and the intelligence on top of it. This role involves owning data ingestion, modeling, governance, and serving across the organization, as well as partnering with Product and Engineering to integrate AI- and LLM-powered features into the product. It is a player-coach role where the individual will spend significant time coding (designing pipelines, building data models, prototyping AI features) while also managing and growing a small team of 1-2 data and AI engineers. The Lead will set the technical direction, establish standards, and be accountable for both the platform and the people. MHC is at a pivotal growth stage, seeking someone to define what data and AI can do for the business and customers, taking end-to-end ownership of green-field data and AI capabilities. This is an opportunity to architect the future of a global SaaS company.

Requirements

Bachelor’s degree in Computer Science, Information Security, or a related field or equivalent years of experience.
7+ years building data and/or backend systems in production, with recent hands-on experience in both data engineering and applied AI/ML.
Proven ownership of a data platform: strong SQL and Python, and experience with modern data tooling (e.g., a cloud warehouse/lakehouse such as Snowflake, BigQuery, Databricks, or Redshift; transformation tools like dbt; and orchestration such as Airflow, Dagster, or Prefect).
Hands-on experience building production AI/ML features, including practical work with LLMs — prompting, retrieval-augmented generation (RAG), embeddings/vector search, evaluation, and integrating models via APIs (e.g., OpenAI, Anthropic) or open-source models.
Solid software engineering fundamentals: data modeling, API design, testing, CI/CD, and building reliable systems on a major cloud (AWS, GCP, or Azure).
Experience leading or mentoring engineers — whether as a manager, tech lead, or senior IC ready to step into people management — and a genuine interest in growing a team.
Strong product sense and communication: able to translate ambiguous business problems into data and AI solutions, and explain trade-offs to technical and non-technical audiences.

Nice To Haves

Experience with document AI / intelligent document processing (OCR, extraction, classification) or NLP over unstructured text.
Experience operating LLMs in production — evaluation/observability, prompt and cost optimization, fine-tuning, or agentic workflows.
Background in B2B SaaS, document/process automation, fintech, or other data-heavy enterprise domains.
Familiarity with data privacy, security, and compliance (e.g., SOC 2, GDPR) in a multi-tenant SaaS environment.
Experience with streaming (Kafka/Kinesis), infrastructure-as-code, and MLOps/LLMOps tooling.

Responsibilities

Architect, design, and build MHC's core data platform — ingestion, transformation, storage, and serving layers — across batch and streaming workloads.
Model data for both analytics and product use cases, creating clean, well-documented, reusable datasets and a semantic layer.
Establish data quality, lineage, observability, and governance practices.
Build the pipelines and feature/embedding stores that feed analytics, ML, and LLM applications.
Mine raw operational and document data into intelligence — metrics, signals, predictions, and document understanding.
Build and evaluate models and retrieval systems (including RAG over documents) that extract, classify, summarize, and reason over MHC's data.
Partner with Product to design and ship LLM-powered features — from prototype to production — with appropriate guardrails, evaluation, and cost controls.
Manage, mentor, and grow a team of 2–4 data and AI engineers; set goals, give feedback, and develop careers.
Set technical standards and a pragmatic roadmap for data and AI; make build-vs-buy decisions and balance speed with long-term maintainability.
Collaborate across Product, Engineering, and business stakeholders to align the data and AI roadmap with company priorities.
Stay hands-on — review code and designs, prototype hard problems, and set the bar for engineering quality.

Benefits

Workplace Flexibility
401(k) Plan with employer match
Medical Plans (co-pay or HSA coverage options)
Dental and Vision Plans
Daycare and Medical FSA/HSA
Group Term Life Insurance
Generous Paid Time Off (PTO) Policies
Employee Assistance Program (EAP)
Additional Life Insurance
Critical Illness Insurance
Accident, Cancer & Hospital Indemnity Insurance
Legal/ID Shield
Pet Insurance
Four weeks of paid paternity leave (after one year of employment, partial eligibility at six months)
Twelve weeks of paid leave for the birth parent