MLOps Platform Engineer (Chennai / Pune)

Money Forward India; Chennai District; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

Money Forward India is seeking an experienced Platform Engineer for MLOps to partner with ML engineers and ensure scalable deployment of ML pipelines and applications.

You will manage cloud-based resources, containerized environments and automated workflows, enabling efficient model training, serving, and monitoring in production.

Qualifications

• Bachelor's degree or higher in CS/engineering or related field.
• 3+ years building core infrastructure for ML/AI projects.
• Experience with DevOps, platform engineering, SRE, cloud-based infra or production ops.

Responsibilities

• Enable ML engineers to develop, train and deploy ML projects using container orchestration, cloud services and CI/CD pipelines.
• Build and maintain scalable ML infrastructure to support training and inference workloads.
• Design, deploy and manage Kubernetes clusters for high availability and performance.
• Develop strategies for GPU optimization, data pipelines and inference endpoints.
• Monitor infrastructure resources and maintain observability for ML workloads.

Skills

Kubernetes

AWS

Python

CI/CD

Terraform

LLMOps

DevOps

Git

Education

Bachelor's degree in Computer Science or related field

Tools

Terraform

Kubernetes

Docker

CI/CD tools

Job description

Overview
Money Forward is developing a variety of services for individuals and corporations to realize our vision, “Becoming the financial platform for all”. In addition, we are working to promote the effective use of data. To further address our customers' needs in the future, we are actively strengthening our development system using AI/ML technology for the main services of each department.
We are looking for a passionate Platform Engineer for MLOps who can work along with our ML platform team and collaborate with ML engineers to ensure deployment, scaling, and maintenance of ML pipelines and applications.
You will help manage cloud-based resources, containerized environments, and automated workflows for AI models. You will contribute to building a scalable AI/ML infrastructure while gaining exposure to the broader MLOps lifecycle and automation.
Attractive points
In this role, you will be at the forefront of the latest technologies in container orchestration, cloud services, and CI/CD pipelines to enable efficient development, training and deployment of ML models.
You will have the autonomy to design and implement optimization strategies, operate and maintain a scalable robust infrastructure tailored for ML projects, and empower ML engineers throughout the MLOps cycle.
Alongside our technical team of talented experienced ML engineers, you will also have the opportunity to contribute to the MLOps cycle, gaining valuable insights in a diverse and dynamic environment.
Responsibilities

• As an MLOps platform engineer, you will play a critical role by enabling our team of ML engineers to develop, train and deploy ML projects efficiently using the latest technologies in container orchestration, cloud services, CI/CD pipelines for data collection, model training and monitoring in production
  

• Building and maintaining a scalable infrastructure to execute ML projects, while committed to results and user value
  

• Develop, design, maintain and manage container orchestration using Kubernetes
  

• Design and execute strategies for GPU optimization, prediction servers, data and training pipelines while ensuring efficient use
  

• Design and build inference platforms while ensuring reliability and high performance
  

• Provision and monitor infrastructure resources
  

• Build and maintain ML workflows and pipelines
  

• Deploy and maintain monitoring services for observability
  

• Ensure compliance with security best practices
  

• Manage and expand LLM serving clusters using stacks like vLLM
  

Requirements

Qualification

• Bachelor's degree in Computer Science, engineering or related field
  

• 3+ years building core infrastructure for ML projects
  

• Demonstrated background in DevOps, Platform Engineering, SRE, cloud-based infrastructure, or managing production operations
  

• Experience supporting Generative AI, LLM, production-level AI/ML, or platforms focused on data-intensive workloads
  

• Deep understanding of the AI application lifecycle, including MLOps, LLMOps, model monitoring, and deployment strategies
  

• Hands‑on experience deploying and providing support for AI services, inference endpoints, and APIs
  

• Experience in managing, designing, implementing and maintaining robust ML infrastructure to support development and inference workloads, ML workflows, training pipelines and versioning
  

• Experience building and scaling machine learning infrastructure
  

• Experience with AWS cloud services
  

• Experience with Kubernetes to deploy and manage containerized applications with high availability and performance
  

• Experience in running and scaling inference clusters
  

• Experience with TerraGrunt or TerraForm, IaC and CI/CD practices
  

• Comfortable taking over legacy projects for operation and maintenance
  

• Proficiency in programming Python
  

• Excellent problem‑solving skills and ability to work in a dynamic environment
  

• Effective communication skills to collaborate with technical and nontechnical members
  

Nice‑to‑have

• Master’s degree in Computer Science, engineering or related field
  

• Production experience operating LLM inference servers such as vLLM (or equivalent serving stacks)
  

• Experience with LLM observability, including the detection of hallucinations, toxicity, and model drift, alongside implementing tracing through OpenTelemetry protocols
  

• Experience with RayServe
  

• Proficiency on KubeFlow and MLFlow for workflows and pipelines
  

• Experience in designing, developing and operating large‑scale AI/ML systems
  

• Certifications in AWS(MLS-C01), Kubernetes(CKA) or relevant technologies
  

• Experience with additional cloud services
  

• Contributions to open‑source projects
  

• Experience in working to improve model performance, including AI/ML model refinement and fine‑tuning
  

• Knowledge of data security standards such as handling personal information, financial/accounting data, PCI DSS, etc., and experience in designing, developing, and operating systems by these requirements.