Data Scientist

Data Scientist

Exp:3+Yrs

Location: bengalore /Gurgoan

Senior Data Scientist with deep hands-on expertise in machine learning, computer vision, generative AI, and cloud-based AI solutions (preferably Azure). The ideal candidate will drive innovation across our AI/ML initiatives and work closely with cross-functional teams to design, develop, and scale intelligent solutions that power real-world applications.
Your role

• Lead end-to-end development of ML/DL models from data preprocessing to model deployment.
• Design and implement advanced solutions using computer vision, NLP, and generative AI models (e.g., Transformers, GANs).
• Apply and experiment with agentic AI approaches to build autonomous decision-making systems.
• Collaborate with engineering, product, and business stakeholders to align AI solutions with business outcomes.
• Work with large-scale datasets and implement MLOps pipelines for automated training, evaluation, and deployment on cloud (Azure preferred).
• Stay up to date with the latest AI research and apply state-of-the-art techniques in production systems.
• Mentor junior data scientists and contribute to AI knowledge sharing across teams.

Skills

• Experience in data science, ML, or AI, with demonstrated project ownership.
• Proficiency in Python and frameworks like TensorFlow, PyTorch, OpenCV, scikit-learn, etc.
• Strong background in generative models (e.g., LLMs, GANs, VAEs) and foundational ML techniques.
• Expertise in computer vision, including object detection, image classification, and segmentation.
• Experience in implementing agentic AI systems using modern orchestration tools and frameworks.
• Hands-on experience with Azure AI services (e.g., Azure ML, Cognitive Services, Azure OpenAI, Azure Blob for data handling).
• Solid understanding of MLOps practices (CI/CD, version control, model registry, monitoring).
• Strong analytical, problem-solving, and communication skills.
• Master's in Computer Science, Data Science, or related field.
• Experience working in cloud-native AI environments (Azure, AWS, or GCP).
• Exposure to vector search engines, knowledge graphs, and retrieval-augmented generation (RAG).
• Publications, patents, or active participation in AI communities or open-source contributions.