[Hiring] Lead, Data AI & Engineering @MHC

Role Description

We're looking for a hands-on Lead, Data & AI Engineering to build the data layer for MHC and the intelligence on top of it. You will own how data is ingested, modeled, governed, and served across the company, and you'll partner with Product and Engineering to ship AI- and LLM-powered features into our product.

This is a player-coach role. You will spend significant time in the code designing pipelines, building data models, and prototyping AI features while also managing and growing a small team of 1-2 data and AI engineers. You'll set the technical direction, establish the standards, and be accountable for both the platform and the people.

Why join us? MHC is at a pivotal moment. With a strong foundation and bold growth ambitions, we're not just scaling, we're transforming. We're looking for someone who isn't here to maintain the status quo, but to define what data and AI can do for our business and our customers.

This isn't a plug-and-play role. You'll have end-to-end ownership of green-field data and AI capability at a company with real data, real customers, and clear problems worth solving. You won't be handed a playbook you'll write it. You'll set the direction, build the foundation, and see your work shipped directly into the product, all while building and leading the team that does it.

If you want to architect the future of a global SaaS company, not just join it this is your opportunity.

Qualifications

• 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.

Requirements

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

Benefits

• Workplace Flexibility
• 401(k) Plan: Deferred and Roth options available to help you save for retirement, with a generous employer match of 50% up to maximum of 4.5% of gross pay.
• Medical Plans: Comprehensive co-pay or HSA coverage options to keep you and your family healthy.
• Dental and Vision Plans: Access to a large network of providers for dental and vision health.
• Daycare and Medical FSA/HSA: Save on eligible daycare and healthcare expenses with our flexible spending and health savings account plans.
• Group Term Life Insurance: Coverage of $50,000 to provide peace of mind.
• Generous Paid Time Off (PTO) Policies: Ample time to relax and recharge.
• Employee Assistance Program (EAP): Support for personal and professional challenges.

• Voluntary Benefits Available:
• Additional Life Insurance
• Critical Illness Insurance
• Accident, Cancer & Hospital Indemnity Insurance
• Legal/ID Shield
• Pet Insurance

• Parental Leave:
• If your state offers a paid family or medical leave program, you will have access to those benefits. For employees in states without a state program, we offer:
• Four weeks of paid paternity leave, available after one year of employment, with partial eligibility beginning at six months.
• Twelve weeks of paid leave for the birth parent, eligibility rules apply.