Principal Data Scientist - AI & Machine Learning
Core
Design, develop, evaluate, and deploy predictive and prescriptive machine learning models and Generative AI solutions for complex client challenges.
Role type
Principal Data Scientist (AI & Machine Learning)
Builds
End-to-end Machine Learning and Generative AI solutions, including RAG systems and intelligent agent-based applications
Domain
Artificial Intelligence, Machine Learning, Generative AI, MLOps
Deliverable
production ML models
Required skills
Machine Learning techniques (Classification, Regression, Clustering, Feature Engineering), Deep Learning, Python, ML frameworks (Scikit-Learn, TensorFlow, Keras, Pytorch), MLOps practices, Cloud platforms (Azure, AWS, GCP, Databricks), Vector Databases (Pinecone, Weaviate, Chroma, Milvus), LLM orchestration (LangChain, LangGraph), Agentic AI frameworks (LlamaIndex, CrewAI, AutoGen), Prompt engineering, Embeddings, Vector Retrieval, Semantic Search, Fine-Tuning, LoRA
Preferred skills
Reinforcement Learning, Optimization techniques, Knowledge Graphs, Graph Machine Learning, Large-scale data pipelines, Apache Spark, distributed data processing, AI architecture definition, international client engagement
Technologies
Azure ML, AWS SageMaker, Google Vertex AI, MLflow, FastAPI, Streamlit, LangSmith, RAGAS
Responsibilities
Design and implement end-to-end Machine Learning and Generative AI solutions; Build and optimize Retrieval-Augmented Generation (RAG) systems and intelligent agent-based applications; Develop scalable model deployment and monitoring solutions using MLOps best practices; Monitor model performance, detect concept drift, and continuously improve deployed systems; Collaborate with software engineering teams to productionize AI applications; Mentor and coach junior Data Scientists and Machine Learning Engineers; Lead technical discussions, knowledge transfer sessions, and client-facing AI engagements
Seniority
Principal, hands-on IC with mentorship and strategy