Senior AI/ML Engineer
$$$$
We are looking for a Senior AI/ML Engineer to design, build, and scale AI-driven solutions within the Palantir Foundry and AIP ecosystem.
In this role, you will take ownership of developing production-ready AI systems, including LLM-powered applications, RAG pipelines, and machine learning models, while collaborating closely with cross-functional teams. You will contribute to architectural decisions, ensure high-quality implementations, and help evolve AI capabilities within enterprise environments.
Key Responsibilities
- Design, develop, and enhance AI-driven solutions, including machine learning models, LLM-based applications, and NLP workflows for analytics, automation, and decision-making
- Build and optimize RAG pipelines, including embeddings, retrieval strategies, chunking approaches, and hybrid search techniques
- Develop AI solutions within Palantir Foundry, leveraging Ontology objects, pipelines, and workflows
- Apply LLMs and GenAI techniques (prompt engineering, fine-tuning, embeddings, retrieval-augmented generation) using Palantir AIP
- Own the end-to-end lifecycle of AI solutions: data preparation, model development, evaluation, deployment, and monitoring
- Collaborate with data engineers to ensure data quality, pipeline efficiency, and scalable data processing
- Integrate AI models into production workflows to deliver business-facing insights and automation capabilities
- Evaluate and improve model and system performance, including accuracy, latency, scalability, and cost-efficiency
- Contribute to architecture decisions, including selection of tools, frameworks, and design patterns for AI systems
- Implement and follow MLOps / LLMOps best practices, including versioning, evaluation frameworks, monitoring, and continuous improvement
- Ensure responsible AI practices, including data privacy, governance, and compliance considerations
- Collaborate with stakeholders to translate business needs into practical and scalable AI solutions
- Mentor junior engineers and contribute to knowledge sharing within the team
Requirements
- 4โ5+ years of experience in AI/ML engineering, applied data science, or related fields
- Strong proficiency in Python and experience with ML frameworks (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow)
- Hands-on experience with LLMs, NLP, or GenAI applications (prompt engineering, embeddings, text processing, summarization, etc.)
- Practical experience with RAG architectures, vector databases, and retrieval strategies
- Strong understanding of the ML lifecycle: data preparation, feature engineering, model training, evaluation, and deployment
- Experience building and deploying production-grade AI systems
- Familiarity with structured and unstructured data processing (tabular, time series, text, documents)
- Familiarity with cloud platforms (AWS, Azure, or GCP) and containerization (Docker, Kubernetes)
- Understanding of MLOps / LLMOps practices, including monitoring, evaluation, and iteration
- Experience working in enterprise data environments with cross-functional teams
- Ability to communicate AI concepts and results to technical and non-technical stakeholders
- Upper-Intermediate English or higher
Nice to Have
- Experience with Palantir Foundry (Ontology, Object Builders, Code Repositories, AIP)
- Experience in regulated industries (e.g., pharma, finance)
- Experience with distributed data processing (e.g., Spark)
- Exposure to multilingual or multimodal AI systems
Required skills experience
| Python | 4 years |
| Machine Learning | 3 years |
| PyTorch | 2 years |
| LLM | 2 years |
| RAG | 2 years |
+ 2 more
| Cloud & Virtualization: AWS | 2 years |
| Docker | 2 years |
Required languages
| English | B2 - Upper Intermediate |
| Ukrainian | Native |
Data Processing, Distributed Systems, Spark, Experience in Pharma, Multimodal AI (text + images), Data Governance, CI/CD for ML systems
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$4000-7000
Average salary range of similar jobs in
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