Data Scientists – AI/ML Engineer

Data Scientists – AI/ML Engineer Must-Have Skills: AI/ML & Deep Learning: • Strong experience with Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn) • End-to-end model building: data prep, training, evaluation, deployment • Experience with NLP, embeddings, transformer architectures, and LLM fine-tuning GPT / LLM / Agentic AI: • Fine-tuning or prompting GPT-based LLMs • Experience building RAG systems (Retrieval-Augmented Generation) • Knowledge of vector databases • Understanding of agentic frameworks (CrewAI, LangChain agents, AutoGen, etc.) • Developing multi-agent systems with tools, memory, and planning loops • Airflow for workflow orchestration • Docker for containerization • Kubernetes for scalable deployment • CI/CD for model deployment • Model monitoring and drift detection • API development (FastAPI, Flask) • Experience with SQL and NoSQL data stores Must have experience in: • Palantir Foundry (data ingestion, transformations, ontology modeling, analytics workflows) • Snowflake (advanced SQL, performance tuning, analytical schema design) • Building scalable ETL/ELT data pipelines (batch, near real-time) • Data modeling for analytics and reporting (layers) • Working with relational and NoSQL databases • Python for data processing and automation • Data quality, governance, and lineage practices • Unsupervised clustering and classification of structured data in DBMS/files and unstructured data • Creating multi-stage analysis/transformation pipelines like Contour in Palantir Foundry • Distributed data processing frameworks like Spark • Querying using GraphQL, Scalding, etc. • Create apps, reports, dashboards like those in Palantir Foundry • Good to have: • dbt, Airflow, Spark or similar orchestration/processing frameworks • BI & visualization tools (Tableau, Power BI, Looker, etc.) • Streaming data platforms (Kafka/Kinesis) • Cloud platforms (AWS/Azure/GCP) • ML feature engineering or analytics support • Agentic AI skills