Hybrid Lead Assistant Manager – Data Science

at EXL

• Job title: Lead Assistant Manager – Data Science
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Azure, Cloud, Docker, Google Cloud Platform, Kubernetes, Python, PyTorch, React, Tensorflow
• Location requirements: Hybrid, Bengaluru, India

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