MLOps & Cloud AI Engineer

Job purpose :

Design, develop, and manage scalable MLOps pipelines for building, training, and deploying predictive and generative LLM models on cloud infrastructure. The role supports automation, monitoring, and optimization of AI workflows across research, clinical, and commercial initiatives to enable data-driven decision-making and operational efficiency.

Duties and responsibilities :

• The successful candidate will work closely with R&D and Corporate stakeholders under Ocugens R&D and Business organization.
• Design, develop, and maintain

  end-to-end MLOps pipelines

  on Azure for training, evaluating, and deploying AI and LLM-based models across multiple environments.
• Integrate and manage

  Azure OpenAI Service, Azure AI Search, Azure Document Intelligence, and Azure Blob Storage

  for model orchestration, data ingestion, and large-scale retrieval workflows.
• Build and manage

  CI/CD pipelines

  using Azure DevOps or GitHub Actions to automate code builds, testing, model deployment, and versioning.
• Implement

  infrastructure-as-code

  for reproducible environment setup using tools like

  Bicep or Terraform

  , ensuring consistency across R&D and production environments.
• Configure

  Azure Container Apps, Container Registry, and Kubernetes clusters

  to support scalable and fault-tolerant model deployment.
• Develop

  data pipelines

  for ingesting, cleaning, and indexing structured and unstructured data into Azure Blob and Azure AI Search for downstream AI workloads.
• Monitor and optimize

  model inference performance, latency, and cost

  , leveraging Azure Monitoring, Application Insights, and autoscaling strategies.
• Track and manage

  token utilization and billing

  for Azure OpenAI and other AI services to ensure efficient resource consumption.
• Implement

  secure storage and access control

  using Azure Key Vault, role-based access, and compliance policies.
• Collaborate with AI, backend, and DevOps teams to

  deploy and maintain LLM and data analytics models

  integrated with enterprise APIs and dashboards.
• Maintain documentation of

  pipelines, deployment workflows, and cost reports

  , supporting transparency and operational readiness.

Qualifications :

• B.Tech/ M.Tech in Computer Science and Engineering
• 2- 4 years of industry experience
• Ability to deliver on tight deadlines
• Demonstrated attention to detail and outstanding organization skills.
• Self-motivated with a commitment to high quality, on-time deliverables.
• Excellent communication and collaboration skills with a team-oriented approach.
• Demonstrated ability to identify and resolve issues and effectively manage timelines.

Working

This position operates in an office setting, in person.

Job may additionally require incumbent to be available outside of these hours to handle priority business needs.

Physical

This is a largely sedentary role.

Direct

N/A