Machine Learning Platform Engineer

Machine Learning Platform Engineer

Key Responsibilities

• Design and develop

  ML/AI platform components

  enabling scalable experimentation, deployment, and monitoring.
• Build

  model serving systems

  with high throughput and low latency for both ML and LLM workloads.
• Develop and optimize

  feature engineering pipelines

  , reusable modules, and feature stores.
• Implement

  context platforms

  ,

  prompt management

  , and

  token management

  for LLM-based applications.
• Ensure robust

  ML observability

  , including model monitoring, logging, metrics, drift detection, and incident management.
• Build modular, reusable

  frameworks, SDKs, and components

  to accelerate model development and deployment.
• Work closely with ML engineers, data scientists, and infra teams to create consistent tooling and workflows.
• Drive

  MLOps and LLMOps

  best practices across the organization, enabling automation and operational efficiency.
• Architect and maintain

  distributed systems

  supporting large-scale model training and inference.
• Contribute to platform roadmap, evaluate new technologies, and ensure system scalability and reliability.
  

Required Skills & Qualifications

• 4- 7 years of hands-on experience in

  ML platform engineering

  , ML infrastructure, or MLOps/LLMOps roles.
• Strong understanding of

  ML/AI platform architecture

  , model lifecycle, and production workflows.
• Expertise in

  model serving

  , scalable deployments, and building low-latency inference systems.
• Proven experience in

  feature engineering

  , feature pipelines, and feature store management.
• Knowledge of

  context management, prompt management, token optimization

  , and LLM deployment practices.
• Strong experience with

  ML observability frameworks

  (metrics, logging, drift, performance monitoring).
• Solid understanding of

  distributed systems

  , high-performance computing, and scaling ML workloads.
• Experience with MLOps tools and practices: CI/CD for ML, workflow orchestration, model versioning, artifact stores.
• Ability to build

  modular frameworks, reusable components, and internal ML libraries

  .

Preferred Skills (Good to Have)

• Experience with GPU clusters, vector databases, or RAG architectures.
• Knowledge of Kubernetes, Docker, and cloud-based ML platforms (AWS/GCP/Azure).
• Experience with ML workflow tools (Kubeflow, Airflow, Flyte, Argo).
• Familiarity with LLMOps platforms and prompt optimization frameworks.
  

What We Offer

• Competitive compensation up to

  75 LPA

  .
• Opportunity to build next-generation ML/LLM platform capabilities.
• Work with top-tier engineering and AI teams solving high-scale challenges.
• Innovation-driven environment with strong focus on automation and platform excellence.