Senior ML Engineer - AI Labs

IDFC FIRST Bank; Bengaluru; On-site; INR 3,000,000 - 4,500,000; Full time

Job summary

IDFC FIRST Bank is seeking a Senior Machine Learning Engineer in Bengaluru to lead Gen AI infrastructure for large-scale model training. You will optimize GPU utilization, orchestrate containers with Docker/Kubernetes, and manage cloud resources across AWS, Azure, and GCP, partnering with data scientists and ML engineers to deliver scalable solutions.

Strong experience with LLMs and deep learning frameworks, plus solid knowledge of distributed systems and MLOps, are required for this role in a

Qualifications

• 4+ years of relevant experience in managing infrastructure for training large-scale ML models.
• Hands-on experience with LLMs and deep learning frameworks.
• Experience in cloud computing, containerization, and distributed systems.
• Prior involvement in Gen AI projects and cross-functional team collaboration.

Responsibilities

• Lead Generative AI projects in a cross-functional team environment.
• Apply advanced ML principles and algorithms, particularly for LLMs such as GPT-4, BERT, and Transformers.
• Utilize deep learning frameworks for model training.
• Maximize GPU utilization and efficiency through GPU architecture optimization.
• Manage and optimize cloud-based resources (AWS, Azure, GCP) for DL model training.
• Implement containerization and orchestration using Docker and Kubernetes.
• Apply parallel and distributed computing for scalable model training.
• Integrate big data technologies like Hadoop and Spark into ML workflows.
• Adopt MLOps practices to manage the end-to-end ML lifecycle.
• Manage infrastructure for multiple ML projects and optimize resource allocation.
• Address training challenges including memory management and data loading.

Skills

GPU architecture

Distributed computing

MLOps

Leadership

Education

BSc/BCA/BTech

Tools

TensorFlow

PyTorch

Keras

Docker

Kubernetes

Hadoop

Spark

Job description

Job Requirements

About the Role

As a Senior Machine Learning Engineer within the Data & Analytics team, you will be responsible for managing and optimizing the training infrastructure for Large Language Models (LLMs). This role demands a deep understanding of GPU architecture, machine learning principles, and distributed computing. You will lead Gen AI initiatives in a cross-functional setup, ensuring efficient resource utilization and timely delivery of large-scale ML projects.

Key Responsibilities

Primary Responsibilities

• Lead Generative AI projects in a cross-functional team environment.
• Apply advanced machine learning principles and algorithms, particularly for LLMs such as GPT-4, BERT, and Transformers.
• Utilize deep learning frameworks like TensorFlow, PyTorch, and Keras for model training.
• Maximize GPU utilization and efficiency through deep knowledge of computer architecture.
• Manage and optimize cloud-based resources (AWS, Azure, GCP) for deep learning model training.
• Implement containerization and orchestration using Docker and Kubernetes.
• Apply parallel and distributed computing principles for scalable model training.
• Integrate big data technologies like Hadoop and Spark into ML workflows.
• Adopt MLOps practices and tools to manage the end-to-end ML lifecycle.

Secondary Responsibilities

• Manage infrastructure for multiple ML projects, especially those involving deep learning models.
• Optimize performance and resource allocation for large-scale ML tasks.
• Handle GPU resource management both on-premises and in the cloud.
• Address challenges in training large models, including memory management, data loading optimization, and hardware troubleshooting.
• Collaborate closely with data scientists and ML engineers to understand infrastructure needs and deliver efficient solutions.

What We Are Looking For

Education

• Graduation in BSC or BCA or B.Tech.

Experience

• 4+ years of relevant experience in managing infrastructure for training large-scale ML models.
• Hands-on experience with LLMs and deep learning frameworks.
• Experience in cloud computing, containerization, and distributed systems.
• Prior involvement in Gen AI projects and cross-functional team collaboration.

Skills and Attributes

• Strong understanding of GPU architecture and optimization techniques.
• Proficiency in TensorFlow, PyTorch, Keras, Docker, Kubernetes, and cloud platforms.
• Knowledge of distributed computing frameworks like Hadoop and Spark.
• Familiarity with MLOps tools and practices.
• Excellent problem-solving and troubleshooting skills.
• Ability to lead technical aspects of projects and ensure error-free, timely deliverables.