Sr AI Developer w/Palantir
155,000
Direct Hire
Direct Hire
Location Requirements:
On-Site
Job ID:
44484
Job Description
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
USC or GC
Salary w/benefit package
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
- 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.
- 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.
- 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.
- 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.
- 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.
USC or GC
Salary w/benefit package
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