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YOUR ROLE

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.

Key Responsibilities

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.

Qualifications & Experience

  • 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.
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YOUR PROFILE

Education

  • 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

Preferred Qualifications

  • 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

Apply now »