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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead, Data AI & Engineering - **Company:** MHC Inc. - **Location:** Burnsville, MN, United States (Remote available) - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Automated Storage and Retrieval Systems, Microsoft Azure, BigQuery, Software as a Service, Code Review, Cyber Security, Continuous Integration, Information Engineering, Data Infrastructure, Document-Oriented Databases, Information Extraction, Python (Programming Language), OpenAI, Standard Sql, Search Technologies, Data Streaming, Retrieval-Augmented Generation, Open-source Models, Large Language Models, Snowflake, Backend, Data Layers, Build Management, Core Data, Information Technology, Apache Kafka, Machine Learning Operations, Api Design, Intelligent Document Processing, Databricks - **Published:** October 26, 2026 - **Apply:** https://www.adzuna.com/details/5894955227 ## About the Role * 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. Preferred experience, skills, and knowledge: * 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. ## Description * 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. What Success Looks Like in This Role: * First 90 days. In 90 days: you understand MHC's data and systems, have a prioritized data & AI roadmap, and have shipped early wins to the data layer. * Six months. In 6 months: a reliable, documented data foundation is in place, and the first AI/LLM-powered feature is in production or in customer testing. * One year. In 12 months: the team is delivering a steady stream of data products and AI features, with strong quality, governance, and a growing, high-performing team.