AI/ML Engineer & Forward Deployed Engineer

The AI/ML Engineer & Forward Deployed Engineer builds and ships the AI, automation, and Palantir Foundry systems that power our internal operations and initiatives. This is a hands-on, builder-focused role: designing data pipelines and ontology, developing AI/ML and LLM-based applications, and working directly and iteratively with business teams as an embedded Forward Deployed Engineer to turn prototypes into production workflows. The role also manages the technical risk and measurable outcomes of what it builds.

Overall 10+ years of work experience Minimum 5-7 years of hands-on experience in AI/ML engineering, data engineering, or forward-deployed/solutions engineering. Demonstrated, production experience with Palantir Foundry is a plus (or a comparable platform such as Databricks or Snowflake) - pipeline building, ontology design, and security modeling. 2+ years of hands-on experience building and shipping LLM-based or generative AI applications (RAG, agents, tool-calling), and/or classical AI techniques such as natural-language processing, computer vision, or deep learning tools (PyTorch, TensorFlow, Hugging Face). Track record of shipping AI, automation, or data-platform systems into production in a corporate or investment environment, not just prototypes. Experience working directly with business or portfolio company teams in an embedded/consultative engineering capacity is a plus. Experience in financial services, investment management, private equity, real estate, or other asset-intensive industries is preferred but not required.

Required Knowledge, Skills and Abilities:

Hands-on, production experience building and deploying workflows, pipelines, and ontology objects on Palantir Foundry or a comparable enterprise data platform (Databricks, Snowflake). Practical experience building AI/ML and LLM-based applications - RAG pipelines, agentic/tool-calling workflows, and model evaluation. Strong software engineering fundamentals: clean pipeline/API design, version control, testing, and production deployment practices. Comfortable working directly and iteratively with business users to translate ambiguous requirements into working systems (forward-deployed / embedded engineering style). Working understanding of data security, access control, and governance patterns for enterprise data platforms. Clear technical communication - able to explain architecture, trade-offs, and status to both engineers and non-technical stakeholders. Analytical mindset with a focus on measurable outcomes, performance, and ROI of what is built.