OverviewnMoney 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.nnnWe 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.nnnYou 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.nnnAttractive 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.nnnYou 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.nnnAlongside 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 energetic environment.nnnResponsibilitiesnnnAs 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 productionnnnBuilding and maintaining a scalable infrastructure to execute ML projects, while committed to results and user valuennnDevelop, design, maintain and manage container orchestration using KubernetesnnnDesign and execute strategies for GPU optimization, prediction servers, data and training pipelines while ensuring efficient usennnDesign and build inference platforms while ensuring reliability and high performancennnProvision and monitor infrastructure resourcesnnnBuild and maintain ML workflows and pipelinesnnnDeploy and maintain monitoring services for observabilitynnnEnsure compliance with security best practicesnnnManage and expand LLM serving clusters using stacks like vLLMnnnnRequirementsQualificationnBachelor's degree in Computer Science, engineering or related fieldnnn3+ years building core infrastructure for ML projectsnnnDemonstrated background in DevOps, Platform Engineering, SRE, cloud-based infrastructure, or managing production operationsnnnExperience supporting Generative AI, LLM, production-level AI/ML,



or platforms focused on data-intensive workloadsnnnDeep understanding of the AI application lifecycle, including MLOps, LLMOps, model monitoring, and deployment strategiesnnnHands-on experience deploying and providing support for AI services, inference endpoints, and APIsnnnExperience in managing, designing, implementing and maintaining robust ML infrastructure to support development and inference workloads, ML workflows, training pipelines and versioningnnnExperience building and scaling machine learning infrastructurennnExperience with AWS cloud servicesnnnExperience with Kubernetes to deploy and manage containerized applications with high availability and performancennnExperience in running and scaling inference clustersnnnExperience with TerraGrunt or TerraForm, IaC and CI/CD practicesnnnComfortable taking over legacy projects for operation and maintenancenProficiency in programming PythonnnnExcellent problem-solving skills and ability to work in a dynamic settingnnnEffective communication skills to collaborate with technical and nontechnical membersnnnNice-to-havennnMasters degree in Computer Science, engineering or related fieldnnnProduction experience operating LLM inference servers such as vLLM (or equivalent serving stacks)nnnExperience with LLM observability, including the detection of hallucinations, toxicity, and model drift, alongside implementing tracing through OpenTelemetry protocolsnExperience with RayServennnProficiency on KubeFlow and MLFlow for workflows and pipelinesnnnExperience in designing, developing and operating larg .

📌 MLOps Platform Engineer (Chennai / Pune)
🏢 Money Forward India
📍 Chennai