Senior ML Engineer - AI Labs
IDFC FIRST Bank · Bengaluru
- Experience4–5 yrs
- SalaryNot disclosed
- Work modeonsite
- Levelmid
- Posted10 Sept 2026
About IDFC FIRST Bank
IDFC FIRST Bank is hiring in Bengaluru in financial services. This role looks for around 4+ years of experience.
Skills
- Generative AI
- machine learning
- Large Language Models
- TensorFlow
- PyTorch
- Keras
- GPU architecture
- AWS
- Microsoft Azure
- Google Cloud Platform
- Docker
- Kubernetes
- distributed computing
- Hadoop
- Apache Spark
- MLOps
The role
A generative AI engineer at a financial services company manages large language model infrastructure, applies machine learning, and optimizes GPU architecture for scalable training. The role also uses PyTorch and Kubernetes to deliver cloud-based deep learning systems.
Full 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.