Senior AI ML Operations Engineer

Team: IT

Location: India

Commitment: Full-time

Workplace Type: remote

Accountabilities

• Design, develop, and maintain stable, scalable, reliable, and secure AI/ML Operations platforms and production pipelines.
• Package, deploy, and manage AI/ML services in production while ensuring deployments are reproducible, maintainable, and interpretable.
• Build and optimize CI/CD pipelines that automate model deployment and accelerate the transition from development to production.
• Provision and manage infrastructure for model training and inference using technologies such as Docker, Kubernetes, serverless platforms, and Infrastructure as Code.
• Implement monitoring and observability for model performance, data drift, latency, reliability, and other production metrics.
• Automate model retraining and data workflows to maintain model quality and operational consistency over time.
• Manage foundation model deployments, fine-tuning workflows, and Retrieval-Augmented Generation architectures involving vector databases and knowledge graphs.
• Support AI/ML workloads using platforms and technologies such as AWS Bedrock, Databricks, MLflow, Mosaic AI, Unity Catalog, and Vector Search.
• Optimize GPU and CPU utilization, infrastructure architecture, and cloud resources to control costs while maintaining efficient, low-latency inference.
• Establish versioning and governance practices for data, code, and machine learning models.
• Implement appropriate security, privacy, and data governance controls to protect sensitive information and support compliance requirements.
• Troubleshoot complex platform, data, infrastructure, and production issues while maintaining high levels of system availability and reliability.
• Partner closely with data scientists, data engineers, software engineers, and other technical stakeholders to bridge model development and production operations.
• Evaluate emerging AI and data technologies and identify opportunities to improve infrastructure, automation, and engineering practices.

Requirements

• Proven experience building and maintaining AI/ML Operations platforms designed for scalability, reliability, efficiency, and security in enterprise environments.
• Strong experience working with large-scale structured and unstructured data, including complex data platforms handling very high data volumes.
• In-depth experience with the Databricks Lakehouse ecosystem and AI/ML workflows involving Databricks and MLflow.
• Hands-on knowledge of Mosaic AI Agent Framework, Unity Catalog, Vector Search, and Knowledge Graph technologies.
• Experience integrating AI/ML Operations pipelines with frameworks such as LangChain and LangGraph.
• Strong programming skills in Python and SQL.
• Hands-on experience with at least one major cloud platform such as AWS, Azure, or GCP, with AWS-based data platforms being particularly valuable.
• Experience with modern software engineering and infrastructure practices, including Kubernetes, CI/CD, Infrastructure as Code, preferably Terraform, monitoring, observability, and alerting.
• Experience with model deployment, retraining automation, production monitoring, data drift detection, and AI/ML system reliability.
• Knowledge of foundation models, fine-tuning, Retrieval-Augmented Generation, vector databases, and AI agent architectures.
• Strong understanding of cloud resource management, infrastructure optimization, and designing systems with cost efficiency in mind.
• Experience supporting highly available and scalable enterprise SaaS or technology platforms.
• Strong analytical, troubleshooting, communication, and problem-solving abilities.
• Ability to collaborate effectively across data science, data engineering, software engineering, and infrastructure teams.
• Legally authorized to work in India on an ongoing basis.

Benefits

• Opportunity to work on advanced AI and machine learning infrastructure at enterprise scale.
• Exposure to modern technologies across MLOps, Databricks, generative AI, cloud platforms, and AI agent ecosystems.
• An environment that encourages innovation, experimentation, and thoughtful technical problem-solving.
• Opportunity to collaborate with experienced engineers, data scientists, and technology professionals across disciplines.
• Support for professional growth and opportunities to expand expertise in emerging AI and data technologies.
• Inclusive environment that values diverse perspectives, continuous learning, and meaningful technical contributions.
• Opportunity to contribute to scalable products and platforms while working with modern engineering practices.