Integrant is looking for game changers to join our team as “Data Scientist - AI & Machine Learning” with below roles and responsibilities:
Responsibilities
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Use mathematics, statistics, machine learning, and artificial intelligence techniques to extract knowledge and insights from structured, semi-structured, and unstructured data.
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Design, develop, evaluate, and deploy predictive and prescriptive machine learning models.
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Conduct open research and experimentation to develop innovative solutions for complex client challenges.
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Engage with clients and stakeholders to understand business needs and translate them into AI and Data Science solutions.
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Design and implement end-to-end Machine Learning and Generative AI solutions.
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Build and optimize Retrieval-Augmented Generation (RAG) systems and intelligent agent-based applications.
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Develop scalable model deployment and monitoring solutions using MLOps best practices.
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Monitor model performance, detect concept drift, and continuously improve deployed systems.
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Collaborate with software engineering teams to productionize AI applications and ensure reliability, scalability, and maintainability.
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Mentor and coach junior Data Scientists and Machine Learning Engineers.
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Lead technical discussions, knowledge transfer sessions, and client-facing AI engagements.
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Stay current with emerging AI, Machine Learning, MLOps, and Generative AI technologies and frameworks.
Requirements
Education & Experience
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10+ years of professional experience, including 7+ years in Data Science, Machine Learning & MLOps
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MSc in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related quantitative discipline.
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PhD is preferred, in related field.
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Experience mentoring, coaching, or leading technical team members.
Data Science & Machine Learning
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Strong foundation in Machine Learning techniques including Classification, Regression, Clustering, Association Rule Mining, Feature Engineering, and Model Evaluation.
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Experience with Deep Learning concepts and frameworks.
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Extensive hands-on experience with Python and the Data Science ecosystem.
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Experience with one or more ML frameworks such as Scikit-Learn, TensorFlow, Keras, or PyTorch.
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Experience conducting research, experimentation, and hypothesis-driven analysis.
MLOps & Production AI
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Experience deploying and managing Machine Learning models in production environments.
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Experience monitoring model performance, detecting concept drift, and driving continuous improvements.
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Hands-on experience with MLOps practices, CI/CD pipelines, model versioning, experiment tracking, monitoring, and observability.
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Experience deploying AI/ML solutions on cloud platforms such as Azure, AWS, GCP, or Databricks.
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Experience with ML platforms and services including Azure ML, AWS SageMaker, or Google Vertex AI.
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Familiarity with deployment and serving tools such as MLflow, FastAPI, and Streamlit.
Generative AI & Agentic AI
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Hands-on experience building Retrieval-Augmented Generation (RAG) solutions and semantic search applications.
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Experience working with Vector Databases such as Pinecone, Weaviate, Chroma, Milvus, or Azure AI Search.
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Experience using LLM orchestration frameworks such as LangChain, LangGraph, or similar technologies.
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Experience working with Agentic AI frameworks such as LlamaIndex, CrewAI, AutoGen, or equivalent.