Data Science & Advanced Analytics

We are seeking a highly skilled Data Scientist with strong expertise in Machine Learning services, Recommender Systems (RS), and Generative AI (LLM & LVM). The role collaborates closely with Data Engineering, Data Science, and ML Engineering teams to design, develop, deploy, and scale intelligent data products and AI solutions., Data Science & Advanced Analytics

• Develop and deploy end-to-end ML models from ideation to production.
• Perform EDA, feature engineering, and model evaluation.
• Build predictive and prescriptive models using statistical and ML techniques.

Machine Learning Services (Primary Focus)

• Design and implement scalable ML pipelines for training, testing, and deployment.
• Work with ML platforms: Azure ML, AWS SageMaker, GCP Vertex AI.
• Implement model lifecycle management: versioning, monitoring, retraining.
• Optimize models for performance, scalability, and reliability.

Recommender Systems (RS)

• Design and build recommendation engines: collaborative, content-based, hybrid.
• Work with large-scale datasets for ranking, personalization, user segmentation.
• Evaluate models using precision@k, recall@k, NDCG.

Generative AI (GenAI - LLM & LVM)

• Build and deploy LLM-powered solutions: chatbots, copilots, document intelligence.
• Implement RAG (Retrieval-Augmented Generation) architectures.
• Work with models such as OpenAI, Azure OpenAI, Hugging Face.
• Develop use cases for text generation, summarization, classification, and image/video understanding (LVM).
• Optimize prompts and manage prompt engineering workflows.

Collaboration with Data Engineering

• Define data requirements; collaborate on data pipeline design.
• Ensure data quality, governance, and availability.
• Work with Spark, Databricks, Hadoop.

ML Engineering & Deployment Support

• Deploy models via APIs/microservices; containerize with Docker, Kubernetes.
• Integrate models into production systems and CI/CD pipelines.

Model Monitoring & Governance

• Monitor model drift, performance degradation, and bias.
• Implement logging, alerting, and explainability tools.
• Ensure Responsible AI: fairness, transparency, interpretability.

• 5-8 years in Data Science, Machine Learning, or AI.
• Proven end-to-end ML model development, deployment, and production support.
• Hands-on predictive modeling using Python and modern ML frameworks.
• Experience with cloud ML platforms (Azure ML, AWS SageMaker, or Google Vertex AI).
• Experience developing GenAI solutions using LLMs, prompt engineering, and RAG frameworks.
• Experience collaborating with Data Engineering and ML Engineering teams.
• Agile experience delivering enterprise-scale AI applications., * B.Tech/B.S./M.S. in Computer Science, Statistics, Mathematics, or related field.

Technical Skills

• Programming: Python (mandatory), SQL
• ML Libraries: Scikit-learn, TensorFlow, PyTorch
• Data Processing: Pandas, NumPy, Spark
• ML & AI: Supervised & unsupervised learning, model optimization
• Recommender Systems: engine design, ranking algorithms, personalization
• Generative AI: LLMs (GPT, Llama, etc.), prompt engineering, RAG (LangChain, LlamaIndex); multimodal AI (LVM) a plus
• MLOps: CI/CD for ML, Docker, Kubernetes, model monitoring
• Data Engineering: pipelines, ETL, data warehousing, * Strong communication, stakeholder management, organizational skills
• Self-motivated, customer-focused, detail-oriented
• Azure ecosystem experience (Azure ML, Databricks)
• Exposure to real-time data processing
• ML/AI/Cloud certifications
• SAP ERP knowledge strongly preferred
• Six Sigma Yellow/Green Belt a plus
• ITIL certification a plus