AI Engineer -Ml Ops-Palantir Foundry-Senior Associate

PwC US - Acceleration Center is seeking a Palantir AI Engineer at the Senior Associate level. In this role, you will play a critical role in building and deploying cutting-edge AI / Machine Leaning / Generative AI applications using Palantir Foundry. The ideal candidate should have strong background in AI / Machine Learning / Generative AI methodologies, designing analytical workflows / application, developing and deploying AI solutions that scale across Foundry’s ontology and operational applications. You will design scalable ML pipelines, engineer high-quality features, integrate models into Foundry’s operational workflows, and partner directly with clients to solve mission-critical problems using data-driven insights. 

Responsibilities: 

• Advanced Analytics & Modeling:  

• Design and develop machine learning models (forecasting, classification, optimization, NLP, clustering, etc.) using Python, Spark, and Foundry-native capabilities.  

• Perform exploratory data analysis to understand client datasets, assess quality, and identify actionable insights.  

• Create reusable feature engineering pipelines and analytical modules within Foundry’s dataset and transformation framework.  

• Evaluate model performance, interpret results, and communicate implications to diverse stakeholders.  

           Machine Learning & AI Engineering: 

• Design, train, and optimize machine learning models using Python, Spark, and Foundry-native capabilities. 

• Build scalable feature engineering pipelines in Foundry using transforms, workflows, and ontology-backed data sources. 

• Implement model evaluation, monitoring, drift detection, and automated retraining workflows. 

• Ensure ML solutions adhere to PwC and client standards for reproducibility, governance, and maintainability. 

          End-to-End ML Pipeline Development: 

• Build ingestion and transformation pipelines supporting ML workloads using Foundry (Code Workbooks, Transform Repositories, Contour/Monocle). 

• Develop automated ML workflows connecting data prep, training, inference, and deployment. 

• Operationalize ML models within Foundry to support real-time or batch decision-making. 

• Apply CI/CD and MLOps best practices for deploying and maintaining production models. 

AIP & Foundry Workflow Development: 

• Build end-to-end analytical workflows in Palantir Foundry, integrating data ingestion, transformations, modeling, and operational decision-support tools. 

• Leverage Foundry components (Code Workbooks, Transforms, Ontology, Operational Apps) to design scalable data products. 

• Support MLOps processes including model deployment, monitoring, versioning, and automated retraining. 

• Knowledge and understanding of Palantir’s Artificial Intelligence Platform (AIP) which connects AI with data and operations. 

Foundry Data Integration, Connectivity Builds: 

• Understanding of data connectivity and integration tools that extend far beyond typical extract-transform-load (ETL) or extract-load-transform (ELT) solutions 

• Leverage Foundry’s data transformation, pipeline management capabilities to build AI ready datasets. 

• Work on an integrated suite of data transformation authoring, change management, data quality, pipeline scheduling, and metadata introspection capabilities that work cohesively to provide an "mission control" for data engineers.  

Client Engagement & Delivery 

• Develop client-ready documentation, dashboards, training materials, and workshops to support adoption. 

• Participate in agile project delivery, sprint planning, and iterative solution refinement 

Requirements: 

• Bachelor's or master’s degree in computer science, Data Science, or a related field. 

• 5+ years of relevant technical/technology experience, with significant expertise in AI / GenAI projects with production deployment experience. 

• Advanced programming skills in Python and fluency in data processing frameworks like Apache Spark. 

• Hands-on experience using Palantir Foundry, including transforms, datasets, ontology, and operational applications. 

• Experience delivering production ML and/or LLM-based solutions. 

• Proficiency in Typescript. 

• Experience deploying ML models or analytical workflows inside Foundry (training pipelines, inference workflows). 

• Familiarity with DevOps/MLOps tools (Git, CI/CD).