Sr AI Developer w/Palantir

Senior AI Developer/Architect – Palantir Data Engineering
BravoTECH, a leader in IT staffing and staff augmentation services, is seeking a hands-on Senior AI Developer/Architect with strong Palantir data engineering and enterprise AI experience.

This position combines approximately 70% hands-on AI development and engineering with 30% business partnership and solution leadership. The ideal candidate can build production-ready AI solutions while clearly communicating technical concepts, business value, and project outcomes to senior leaders.
Top Skills

• Palantir Foundry and Artificial Intelligence Platform (AIP)
• Palantir data pipelines, Ontology, and application development
• Azure OpenAI and Azure Machine Learning
• Generative AI, LLMs, RAG, agents, and copilots
• Python and production-grade software development
• Data engineering and scalable AI pipelines
• MLOps, CI/CD, and model monitoring
• Kubernetes, Docker, AKS, and GPU workloads
• Cloud security and AI governance
• Terraform, Bicep, or ARM templates
• Business-facing communication and technical leadership

Key Responsibilities AI Development and Engineering – 70%

• Design, build, and deploy scalable AI/ML solutions, including predictive models, generative AI, NLP, optimization, and intelligent automation.
• Develop enterprise AI applications using Palantir Foundry, Palantir AIP, Azure OpenAI, Azure Machine Learning, and Azure AI Services.
• Build and manage Palantir data pipelines, datasets, transformations, Ontology objects, actions, and operational workflows.
• Integrate structured and unstructured enterprise data into Palantir for analytics, AI applications, and business decision-making.
• Architect production-ready pipelines covering ingestion, transformation, feature engineering, model deployment, monitoring, and retraining.
• Develop LLM-powered applications using RAG, prompt engineering, agents, copilots, vector databases, and model orchestration frameworks.
• Write scalable, maintainable, and production-grade Python code.
• Implement MLOps capabilities, including automated testing, CI/CD, model versioning, drift detection, performance monitoring, and lifecycle management.
• Develop secure APIs and scalable inference endpoints for AI applications.
• Deploy containerized and GPU-intensive workloads using Kubernetes, Docker, AKS, or Azure VM Scale Sets.
• Integrate Palantir and AI solutions with Azure Data Factory, Azure Synapse, Databricks, APIs, and other enterprise data platforms.
• Optimize AI and data workloads for reliability, scalability, performance, and cost efficiency.
• Embed security, explainability, data governance, privacy, and responsible AI standards into every solution.

Business Partnership and Solution Leadership – 30%

• Translate ambiguous business challenges into structured AI and Palantir solution designs.
• Partner with Finance, Operations, Sales, Marketing, IT, and other business teams to identify high-value use cases.
• Lead discovery sessions, architecture discussions, whiteboarding workshops, and technical demonstrations.
• Explain AI concepts, Palantir capabilities, technical tradeoffs, and model outputs to non-technical stakeholders.
• Define measurable success criteria and quantify the expected ROI of proposed solutions.
• Prioritize AI initiatives based on business value, technical feasibility, cost, and implementation risk.
• Provide technical leadership across AI architecture, data engineering, cloud deployment, and MLOps.
• Mentor junior developers and help strengthen AI knowledge across the organization.
• Evaluate emerging AI and cloud technologies and recommend scalable enterprise solutions.

Security and Governance

• Implement RBAC, managed identities, Key Vault, private endpoints, encryption, network isolation, and least-privilege access.
• Establish appropriate data access controls and governance within Palantir Foundry and AIP.
• Ensure solutions comply with enterprise security, data privacy, auditability, and responsible AI requirements.
• Monitor AI workloads for latency, utilization, model drift, failures, and operational reliability.

Required Qualifications

• 7+ years of software development, data engineering, or cloud engineering experience.
• 3+ years of experience building and deploying AI or machine-learning solutions in production.
• 2+ years of hands-on Palantir Foundry data engineering experience.
• Experience with Palantir AIP, Ontology, Pipeline Builder, data transformations, operational workflows, or related Palantir capabilities.
• Strong proficiency in Python and frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Experience with LLM technologies such as OpenAI APIs, Hugging Face, LangChain, vector databases, RAG, and AI agents.
• Experience with Azure OpenAI, Azure Machine Learning, or comparable cloud AI services.
• Strong understanding of data modeling, ingestion, transformation, data quality, lineage, and governance.
• Experience deploying AI/ML models into secure, production cloud environments.
• Experience with Kubernetes, Docker, MLOps, CI/CD, and scalable compute or GPU deployments.
• Knowledge of cloud networking, identity management, security architecture, and enterprise governance.
• Experience with Terraform, Bicep, ARM templates, or comparable Infrastructure-as-Code tools.
• Demonstrated ability to communicate complex technical concepts to business leaders.
• Proven experience working directly with business stakeholders and leading solution-design discussions.

What Success Looks Like

• AI solutions are deployed into production and generate measurable business value.
• Palantir data pipelines and Ontology models provide trusted, reusable data foundations.
• Time from initial use-case identification to production implementation is reduced.
• Business leaders understand and trust the AI solutions being delivered.
• Secure, scalable architecture supports repeatable AI delivery across multiple business functions.

4 days onsite
USC or GC
Salary w/benefit package