Degree in computer science, Statistics, and Data Science preferred. Master’s degree and 6+ years experience Or Bachelor’s degree and 8+ years’ experience
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Cloud Computing certificate preferred
Experience with big data ecosystems (Spark, Hadoop) and large-scale data processing.
Strong background in data engineering and building scalable data platforms.
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Advanced proficiency in Python and modern ML/AI frameworks (PyTorch, TensorFlow, Hugging Face, LangChain or similar).
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Experience designing robust evaluation and validation systems, including automated evals, human-in-the-loop, safety testing, and monitoring frameworks.
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Extensive experience with RAG architectures, vector databases, and knowledge-grounded systems.
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Strong understanding of agentic AI frameworks, including orchestration, planning, memory, and tool use.
Knowledge of advanced statistical modeling, experimentation design, and causal inference.
Experience with NLP, semantic search, embeddings, and vector search systems.
Familiarity with Responsible AI practices, including fairness, explainability, governance, and regulatory considerations.
Experience with cloud-native AI/ML services (AWS, Azure, GCP) and cost/performance optimization.
Experience with Databricks platform for enterprise-scale ML and GenAI workloads.
Exposure to advanced evaluation techniques, including red-teaming, adversarial testing, and synthetic data generation.
Experienced with data modeling and performance tuning for both OLAP and OLTP databases
Experienced with Apache Spark, Apache Airflow and Databricks platform