Design, build, and deploy production-grade ML models with end-to-end pipelines and MLOps best practices to ensure scalability, reproducibility, and continuous improvement.
Lead the design and deployment of GenAI solutions leveraging LLMs, RAG, prompt engineering, and fine-tuning to build intelligent assistants, conversational agents, and knowledge retrieval tools.
Analyze large-scale datasets using statistical methods and advanced analytical frameworks to uncover actionable patterns and measure AI/ML impact.
Partner cross-functionally to deliver high-impact AI/ML use cases—including predictive analytics, anomaly detection, and workflow automation—across enterprise digital workplace platforms.
Serve as a subject matter expert in ML and GenAI, mentoring team members, driving actionable business recommendations, and contributing to responsible AI governance frameworks.

5+ years of hands-on experience delivering production-level ML/AI solutions with strong expertise in Generative AI, LLMs, RAG architectures, and prompt engineering.
Advanced proficiency in Python, ML/AI frameworks (scikit-learn, TensorFlow, PyTorch, Hugging Face, LangChain), NLP techniques, SQL, and large-scale data platforms (Databricks, Spark, BigQuery).
Hands-on experience with MLOps practices (model versioning, CI/CD, experiment tracking, monitoring) and cloud platforms (Azure, AWS, GCP) and their AI/ML services.
Strong foundation in statistics, probability, and experimental design with the ability to apply rigorous analytical methods to real-world problems.
Excellent storytelling and communication skills with the ability to convey complex technical concepts to both technical and non-technical stakeholders.

Senior Data Scientist – ML/AI Digital Workplace Experience

Key skills