AI ML Ops Software Engineer

. Design, develop, and maintain stable and reliable AI/ML Ops platforms and pipelines .

Posted 9/25/2026full-timeBangalore • IndiaMid-LevelSeniorWebsite

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformKubernetesPythonSQLTerraformUnity

Key responsibilities & impact

Design, develop, and maintain stable and reliable AI/ML Ops platforms and pipelines
Package and deploy AI/ML services to production, ensuring reproducibility and interpretability
Design and implement automated CI/CD pipelines to accelerate model deployment
Provision and optimize infrastructure for model training and serving using Docker, Kubernetes, or serverless platforms
Implement post-deployment monitoring for model performance, data drift, and latency
Automate retraining and data pipeline workflows
Manage deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation stacks, including vector databases and knowledge graphs
Optimize GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference
Collaborate with data scientists, data engineers, and software engineers to connect model development with production
Manage versioning for data, code, and models using tools such as MLflow
Implement data security measures and ensure compliance with data governance policies
Evaluate emerging data technologies and identify infrastructure innovation opportunities
Diagnose and resolve complex data-related issues
Perform other duties as assigned

What you’ll need

Minimum experience of 4–6 years in AI/ML Ops
Experience building and maintaining scalable, reliable, efficient, and secure AI/ML Ops platform systems
In-depth experience with AI/ML frameworks and tools involving large petabytes of data with the Databricks Lakehouse ecosystem
Experience with AI/MLOps workflows on Databricks, MLflow, Mosaic AI Agent Framework, Unity Catalog, Vector Search, and Knowledge Graph
Knowledge of AI/ML frameworks such as LangChain and LangGraph for AI/ML Ops pipeline integration
Hands-on experience with at least one major cloud provider: AWS, Azure, or GCP
Experience in an AWS-hosted data platform is preferable
Proficiency in Python and SQL
Experience with Kubernetes, CI/CD, infrastructure-as-code tools (preferably Terraform), observability, monitoring, and alerting
Experience with enterprise SaaS software solutions requiring high availability and scalability
Experience handling large-scale structured and unstructured data from varied data sources
Experience with solution cost optimization and design-to-cost principles
Legally eligible to work in India on an ongoing basis
Experience with Monte Carlo is preferable
Experience with AWS Bedrock is preferable

Comp & perks