Associate Principal - Data Sciences

Experience:

Location:

Type:

Role Summary

R&D Specialist

Generative AI (GenAI)

Computer Vision

Predictive Modeling

Retail and Consumer space

industrial applications, digital twins, and autonomous systems

Key Responsibilities

• Lead and contribute to applied research in Generative AI, including LLMs, Vision-Language Models (VLMs), Multimodal AI, and Prompt Engineering.
• Collaborate with product and engineering teams to translate GenAI research into real-world use cases for industries like manufacturing, automotive, oil & gas, and healthcare.
• Conduct R&D in Traditional AI domains such as:
• Computer Vision: object detection, segmentation, OCR
• Predictive Analytics: RUL, anomaly detection, forecasting
• Build and evaluate AI models using tools such as PyTorch, TensorFlow, Hugging Face, LangChain, OpenCV, Scikit-learn, etc.
• Publish internal white papers, IP disclosures, and present research outcomes to leadership and stakeholders.
  

Required Skills & Experience

• 7–12 years of hands-on experience in AI/ML R&D, including a minimum of 2–3 years in GenAI.
• Strong experience with LLMs: fine-tuning, model evaluation, vector databases, and inference pipelines.
• Deep expertise in at least one traditional domain:
• Computer Vision (e.g., CNNs, YOLO, Mask R-CNN) OR
• Predictive Modeling (e.g., time-series, survival models).
• Experience deploying models in Edge/Cloud/MLOps pipelines and working with enterprise data platforms.
• Strong programming skills in Python with experience in notebook-based prototyping, REST APIs, and ML libraries.
  

Good To Have / Preferred

• Knowledge of Agentic AI, Digital Twins, or Simulation-based learning.
• Experience in industrial AI domains (e.g., smart factory, quality inspection, predictive maintenance).
• Exposure to generative vision models like Diffusion Models, GANs, or NeRFs.
• Experience collaborating in multidisciplinary R&D teams.
• Contributions to open-source, patents, or publications in AI/ML.
  

Skills

Mandatory:

• Git
• Jenkins
• Kubernetes
• Cloud Computing
• Industrial AI – LLM
• Docker
• Generative AI / Open AI / Vector DB
• Industrial AI – LLM Ops
• Industrial AI – Machine Learning (ML)
• AR / VR / MR