Data Scientist developing machine learning and AI solutions at STATSports. Turning GPS and IMU sensor data into actionable insights for athlete performance and decision - making.
About the role
- Data Scientist role designing and deploying scalable AI systems for multi-user applications. Collaborating with cross-functional teams using advanced tools and frameworks in AI.
Responsibilities
- Lead the architecture, development, and deployment of scalable machine learning systems, focusing on real-time inference for LLMs serving multiple concurrent users.
- Optimize inference pipelines using high-performance frameworks like vLLM, Groq, ONNX Runtime, Triton Inference Server, and TensorRT to minimize latency and cost.
- Design and implement agentic AI systems utilizing frameworks such as LangChain, AutoGPT, and ReAct for autonomous task orchestration.
- Fine-tune, integrate, and deploy foundation models including GPT, LLaMA, Claude, Mistral, Falcon, and others into intelligent applications.
- Develop and maintain robust MLOps workflows to manage the full model lifecycle including training, deployment, monitoring, and versioning.
- Collaborate with DevOps teams to implement scalable serving infrastructure leveraging containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS, GCP, Azure).
- Implement retrieval-augmented generation (RAG) pipelines integrating vector databases like FAISS, Pinecone, or Weaviate.
- Build observability systems for LLMs to track prompt performance, latency, and user feedback.
- Work cross-functionally with research, product, and operations teams to deliver production-grade AI systems handling real-world traffic patterns.
- Stay updated on emerging AI trends, hardware acceleration techniques, and contribute to open-source or research initiatives where possible.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related fields.
- 6–7 years of experience in machine learning engineering, applied AI, or MLOps roles.
- Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers.
- Deep knowledge of NLP, transformer-based architectures, and generative AI models.
- Hands-on experience with scalable LLM inference optimization using tools like vLLM, Groq, Triton Inference Server, TensorRT, or ONNX Runtime.
- Proven ability to serve AI models to concurrent users with low latency and high throughput.
- Experience in deploying ML systems on cloud platforms (AWS, GCP, Azure).
- Expertise in containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.
- Familiarity with vector search technologies (FAISS, Pinecone, Weaviate) and RAG implementations.
Benefits
- Remote/PAN India options
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