The Role

We are hiring our first Lead AI Architect to design, build, and run the centralized data and AI platform that powers Banyan. The cornerstone of the work is a centralized data model, a Banyan data lake, that consolidates the data we already have and unlocks safe AI experimentation for our internal functional teams: Finance, M&A, Business Development, and others.

This role is modeled on a classical Enterprise Architect role and adapted for an AI-first world. You will set the target-state architecture, choose the patterns we standardize on, write code yourself, and own the result. You will partner with our embedded business analysts and the leaders of each functional vertical so the platform stays grounded in real use cases, not abstractions.

This is a player-coach role. You start hands-on. Over time you build and lead a small platform team.

What You Will Build

• A centralized data lake on AWS: the single, governed source of truth for internal Banyan data. Designed for analytics, ML, and AI experimentation.
• A reference architecture for AI: patterns and building blocks our verticals use to go from idea to safe production: model selection, RAG, evaluation, observability, cost controls.
• Guardrails that make experimentation safe: data classification, access tiering, sandboxes, audit logging, and clear rules for what can be sent where.
• A platform team and operating model: the team, standards, and rituals that keep the platform reliable and improving as adoption grows.

What You Will Do

• Define and own the target-state data and AI architecture for Banyan. Make the trade-offs explicit and write them down.
• Design and build the AWS-centric data lake. Set the patterns for storage, cataloging, query, ingestion, modeling, and lineage. Likely stack: S3, Glue, Lake Formation, Athena, Redshift, Iceberg or similar open table formats, plus Terraform or CDK for infrastructure as code.
• Stand up the AI experimentation layer: model access via Anthropic, OpenAI and other LLM
• Establish data contracts, schema standards, naming conventions, and a domain model that holds up as we add sources and acquire companies.
• Partner with our Senior Business Analysts embedded in Finance, M&A, and Business Development to translate use cases into platform requirements and to unblock their work.
• Define security, privacy, and AI safety guardrails. Classify data, design access tiering, and set the rules for handling PII, regulated data, and confidential portfolio information.
• Stay hands-on. Write code, ship infrastructure, run code reviews, and debug production issues. Architects who do not build lose touch.
• Build the team including vertical aligned Senior Business AI Analysts. Find suitable contractors for data platform engineers, ML platform engineers, and data engineers. Mentor them. Set the bar for engineering quality.
• Own platform reliability, observability, and cost. Set SLOs, monitor usage, and keep the bill rational as we scale.
• Represent Banyan's AI and data architecture to our executive team, our portfolio companies, and external partners.

First-Year Outcomes

• A production-ready, governed data lake foundation deployed on AWS, with at least three priority data domains landed and modeled.
• At least two functional verticals onboarded to the platform with safe, audited data access for analytics and AI experimentation.
• A documented reference architecture, security model, and standards library that new use cases and new hires can plug into.
• At least one AI use case running in production through the platform, with monitoring, evaluation, and a clear ROI story.
• First one to two team hires made and ramped.

Who You Are

• 10+ years building data and AI platforms in production at meaningful scale. You have shipped, not just diagrammed.
• Deep AWS expertise. Hands-on with S3, IAM, KMS, networking, Glue, Lake Formation, Athena, Redshift, SageMaker, Bedrock, and EKS or ECS.
• Strong data architecture chops: lakehouse patterns, open table formats (Iceberg, Delta, or Hudi), data modeling, ingestion frameworks (Fivetran, Airbyte, custom), orchestration (Airflow, Dagster, or similar), and lineage.
• Real experience with modern AI/ML platform patterns: feature stores, model registries, experiment tracking, prompt and evaluation frameworks, retrieval-augmented generation, and basic agentic workflows.
• A working point of view on AI safety: data classification, PII handling, access tiering, evaluation, and model-risk controls.
• Hands-on coder. Strong Python and SQL. Comfortable in Terraform or AWS CDK. Reads logs, fixes issues, ships pull requests.
• Player-coach experience. You have led small teams, mentored engineers, set hiring bars, and grown people.
• A strong communicator. You can drive an architecture decision review with engineers and explain trade-offs to executives in the same week.
• Bachelor's degree in computer science, engineering, or a related field. A master's degree is a plus, not a requirement.

Bonus Points

• Experience in private-equity-backed or holding-company environments with multiple business units and inconsistent source systems.
• Experience designing AI guardrails for non-public, regulated, or competitively sensitive data.
• Track record of treating internal platforms as products, with named users, SLAs, and a real adoption strategy.
• Experience with Snowflake or Databricks. Useful when bridging to portfolio company data without forcing a single stack.
• Past experience as the first or early platform hire at a growth-stage company.

Why Banyan

• Greenfield with real budget. You set the foundation, you do not inherit a tangle.
• Real ownership of the AI and data architecture for an entire holding company.
• A long-term horizon. We do not optimize for the next quarter, so the platform you build can compound.
• A team that treats AI as a serious capability, not a buzzword. We have already built internal AI tooling we use every day.
• A culture of accountable autonomy. We trust you to do the work.
• Competitive compensation, benefits, and meaningful long-term upside.

Quick Facts

How to Apply

Send your resume and a short note describing a data or AI platform you have architected. Tell us the choices you made, what shipped to production, and what you would do differently if you were starting over today.

We look forward to hearing from you.