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
- Lead Data Scientist architecting and deploying scalable machine learning systems for real-time applications. Collaborate with cross-functional teams to deliver high-performance AI solutions in Bengaluru, India.
Responsibilities
- Lead the architecture, development, and deployment of scalable machine learning systems for real-time inference.
- Optimize inference pipelines using high-performance frameworks like vLLM, Groq, ONNX Runtime, Triton Inference Server, and TensorRT.
- Design and implement agentic AI systems utilizing frameworks such as LangChain, AutoGPT, and ReAct.
- Fine-tune, integrate, and deploy foundation models including GPT, LLaMA, Claude, Mistral, Falcon.
- Develop and maintain robust MLOps workflows for the model lifecycle.
- Collaborate with DevOps teams for scalable serving infrastructure.
- Implement retrieval-augmented generation (RAG) pipelines with vector databases.
- Build observability systems for LLMs to track performance metrics.
- Work cross-functionally with various teams to deliver production-grade AI systems.
- Stay updated on emerging AI trends and contribute to relevant initiatives.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related fields.
- 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.
- Experience with agent-based AI frameworks, autonomous workflows, and prompt chaining (preferred).
- Knowledge of fine-tuning methods like LoRA, PEFT, RLHF (preferred).
Benefits
- Flexible work arrangements (remote/PAN India options)
- Professional development opportunities
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