Lead Data Scientist - AI & Machine Learning

We Are Hiring!

Integrant is looking for game changers to join our team as Data Scientist - AI & Machine Learning with below roles and responsibilities: Use mathematics, statistics, machine learning, and artificial intelligence techniques to extract knowledge and insights from structured, semi-structured, and unstructured data.

Design, develop, evaluate, and deploy predictive and prescriptive machine learning models. Conduct open research and experimentation to develop innovative solutions for complex client challenges. Engage with clients and stakeholders to understand business needs and translate them into AI and Data Science solutions.

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 and ensure reliability, scalability, and maintainability. Mentor and coach junior Data Scientists and Machine Learning Engineers.

Lead technical discussions, knowledge transfer sessions, and client-facing AI engagements.

Stay current with emerging AI, Machine Learning, MLOps, and Generative AI technologies and frameworks Requirements Education & Experience 10+ years of professional experience, including 7+ years in Data Science, Machine Learning & MLOps MSc in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related quantitative discipline PhD is preferred, in related field.

Experience mentoring, coaching, or leading technical team members Data Science & Machine Learning Strong foundation in Machine Learning techniques including Classification, Regression, Clustering, Association Rule Mining, Feature Engineering, and Model Evaluation Experience with Deep Learning concepts and frameworks Extensive hands-on experience with Python and the Data Science ecosystem Experience with one or more ML frameworks such as Scikit-Learn, TensorFlow, Keras, or PyTorch Experience conducting research, experimentation, and hypothesis-driven analysis MLOps & Production AI Experience deploying and managing Machine Learning models in production environments Experience monitoring model performance, detecting concept drift, and driving continuous improvements Hands-on experience with MLOps practices, CI/CD pipelines, model versioning, experiment tracking, monitoring, and observability Experience deploying AI/ML solutions on cloud platforms such as Azure, AWS, GCP, or Databricks Experience with ML platforms and services including Azure ML, AWS SageMaker, or Google Vertex AI Familiarity with deployment and serving tools such as MLflow, FastAPI, and Streamlit Generative AI & Agentic AI Hands-on experience building Retrieval-Augmented Generation (RAG) solutions and semantic search applications Experience working with Vector Databases such as Pinecone, Weaviate, Chroma, Milvus, or Azure AI Search Experience using LLM orchestration frameworks such as LangChain, LangGraph, or similar technologies Experience working with Agentic AI frameworks such as LlamaIndex, CrewAI, AutoGen, or equivalent Experience implementing MCP (Model Context Protocol), tool calling, and function-calling workflows Strong understanding of prompt engineering techniques and LLM optimization Experience evaluating LLM applications using frameworks such as LangSmith, RAGAS, or similar tools Experience with Embeddings, Vector Retrieval, Semantic Search, Fine-Tuning, and LoRA techniques Nice to Have: Advanced AI & Data Science Reinforcement Learning (RL) Optimization techniques, including single-objective and multi-objective optimization Stochastic Local Search methods Knowledge Graphs and Graph Machine Learning Cloud & Data Engineering Experience building large-scale data pipe