Note: The job is a remote job and is open to candidates in USA. Accion Labs is recruiting for an unnamed global asset management firm seeking a Forward Deployed AI Engineer. The role builds and deploys Palantir Foundry workflows, data pipelines, and AI/LLM applications, working with business teams to turn prototypes into production workflows and deliver measurable outcomes.
Responsibilities
- 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
Skills
- 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
- Minimum 10+ years of hands-on experience in AI/ML engineering, data engineering, or forward-deployed/solutions engineering
- 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
- 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
- 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
Benefits
- 100% remote
Company Overview
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