Lead Assistant Manager-Data Science-Data Scientist

1 week ago

Bengaluru, Karnataka, India EXL Full time ₹ 12,00,000 - ₹ 36,00,000 per year
Description
  • 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.
Responsibilities
  • Architect and deploy scalable LLM-powered applications with low-latency, high-throughput inference.
  • Optimize model serving using state-of-the-art tools such as vLLM, Groq, ONNX Runtime, Triton, and TensorRT.
  • Design and develop agentic AI frameworks and autonomous workflows using LangChain, AutoGPT, and ReAct.
  • Fine-tune and integrate large foundation models (GPT, Claude, LLaMA, etc.) into production systems.
  • Build and maintain MLOps pipelines covering training, deployment, monitoring, and lifecycle management.
  • Collaborate with DevOps for containerized deployment and scalable orchestration using Kubernetes and Docker.
  • Implement and optimize retrieval-augmented generation (RAG) solutions using vector databases like FAISS, Pinecone, or Weaviate.
  • Monitor and evaluate model performance, latency, and user interaction metrics to ensure system reliability and efficiency.
  • Work closely with cross-functional teams to translate AI research into practical, scalable solutions.
Qualifications

Required:

  • 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.

Preferred:

  • Experience with agent-based AI frameworks, autonomous workflows, and prompt chaining.
  • Knowledge of fine-tuning methods like LoRA, PEFT, RLHF.
  • Contributions to open-source AI/ML projects or active participation in AI research communities.
  • Understanding of hardware acceleration for GPU/TPU inference optimization.
  • Exposure to event-driven and streaming data systems like Kafka or Redis Streams.

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