AI/ML Engineer

3 weeks ago

Goregaon, India sportzinteractive Full time

About the role

Sportz Interactive is committed to harnessing the power of AI to drive innovation across our sports technology offerings. We're looking for a Head of AI/ML & Data Science to lead and scale our end-to-end AI practice. This pivotal role will be responsible for establishing the strategic and technical foundation of our AI/ML initiatives— spanning classical statistics, predictive modelling, computer vision, and generative AI.

As the AI/ML leader in a growing organization, you will have the opportunity to architect and implement cutting-edge solutions across LLM workflows (including RAG and fine-tuning), and Image/Video/Audio generative AI pipelines. You'll play a hands-on role in shaping the data science roadmap, building a high-performing team, and embedding intelligence into our core products and platforms.

Key Responsibilities

  • Define and execute a multi-year AI roadmap aligned to business objectives.
  • Architect scalable training/fine-tuning pipelines using pre-trained and custom models.
  • Build and mentor a cross-functional team of ML engineers, data scientists & MLOps specialists.
  • Establish production-grade MLOps: versioning, CI/CD, monitoring, experiment tracking.
  • Partner with stakeholders to translate use cases (e.g., CV inspection, LLM agents) into deliverables.
  • Ensure model quality, fairness, compliance, and reproducibility.

Background and Experience

  • 8+ years of software engineering experience, with 5+ years in ML/AI.
  • Hands-on Python proficiency, with deep experience in PyTorch and/or TensorFlow (or equivalent frameworks).
  • Proven experience training deep learning models from scratch, including dataset design, architecture selection, hyperparameter tuning, and end-to-end validation
  • Proven experience applying computer vision techniques for image and video processing—object detection, segmentation, inpainting, style transfer, temporal coherence optimization.
  • Strong background across the ML lifecycle: data preparation, feature engineering, modeling, deployment, and monitoring.
  • Experience with RAG and LLM fine-tuning workflows.
  • Proven track record building end-to-end MLOps platforms (e.g., Kubeflow, MLflow, TFX, SageMaker, or similar), including model versioning, CI/CD for training and serving, experiment tracking, and monitoring
  • Excellent leadership, communication, and stakeholder management skills.

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