100% remote - Forward Deployed AI Engineer (Palantir Foundry, GenAi, LLM's, Embedded/Forward-Deployed Collaboration) - Direct Client - Immediate Interviews

One of my direct clients, a global asset management firm, is looking for a Forward Deployed AI Engineer .

100% remote

Mandatory Skills - Palantir Foundry, GenAi, LLM’s, Embedded/Forward-Deployed Collaboration

Duration - 6+ months, long-term, C2C/W2/FTE

Client details will be shared post technical call

The Forward Deployed Engineer builds and ships the AI, automation, and Palantir Foundry systems that power Client’s 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.

Essential Functions:

· Design, build, and deploy Palantir Foundry workflows, including data pipelines, ontology objects/links/actions, and security models.

· Work directly and iteratively with business stakeholders in an embedded, forward-deployed model — prototyping, gathering feedback, and hardening workflows into production applications.

· Design, build, and deploy AI/ML and LLM-based applications (retrieval-augmented generation, tool-calling agents, and evaluation harnesses) that solve concrete business problems.

· Build and maintain integrations between Foundry and upstream/downstream systems (e.g., Databricks, Oracle Fusion HCM, GL/accounting systems), including API, JDBC, and OAuth-based connectivity.

· Identify and scope high-value AI and automation opportunities across underwriting, asset management, corporate/back-office operations, and portfolio company operations, then build the working solution.

· Configure and extend AI platforms such as Claude Enterprise and ChatGPT Enterprise, including connectors, agentic workflows, and tool integrations.

· Establish reusable engineering patterns (pipeline templates, ontology conventions, RAG/agent frameworks) that other teams can build on.

· Own data security, access control, and technical governance for the systems built, in partnership with InfoSec and Compliance.

· Track and report on the performance, adoption, and measurable ROI of deployed AI, automation, and Foundry workflows.

· Provide hands-on technical training and documentation so business teams can operate and extend the workflows built for them.

· Stay current on AI/ML tooling, model capabilities, and Foundry platform updates, and evaluate them for practical fit.

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.

Experience:

· Minimum 10+ 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, …