Senior AI ML Operations Engineer

Job Description/ Responsibilities:

Designing, developing and maintaining stable and reliable AI/ML Ops platforms / pipelines

Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable

CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools

Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms

Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable

Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time.

Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with AWS Bedrock is preferable

Resource Optimization: Manage GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference for users

Collaboration: Work closely with data scientists, data engineers, and software engineers to bridge the gap between model development and production.

Version Control & Governance: Manage versioning for data, code, and models using tools like MLflow.

Security & Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data

Technology Evaluation and Innovation: Staying abreast of emerging data technologies and exploring opportunities for innovation to improve the organisation’s data infrastructure

Troubleshooting and Problem Solving: Diagnosing and resolving complex data-related issues, ensuring the stability and reliability of the data platform

Perform other duties as assigned

Required Skills:

Enterprise SaaS software solutions with high availability and scalability

Solution handling large scale structured and unstructured data from varied data sources

Experience in building and maintaining AI/ML Ops platform systems ensuring scalability, reliability, efficiency and security

In depth experience in AI/ML Frameworks and tools involving large Petabytes of data with Databricks Lakehouse ecosystem

AI/MLOps workflows on Databricks , MLFlow, Mosaic AI Agent Framework, Unity Catalog, Vector Search, Knowledge Graph

Knowledge of AI/ML frameworks like LangChain, LangGraph for AI/ML Ops pipeline integration

Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). Experience in AWS hosted data platform is preferable

Programming languages like Python and SQL

Modern software engineering practices like Kubernetes, CI/CD, IAC tools (Preferably Terraform), Observability, monitoring and alerting

Solution Cost Optimisations and design to cost

Legally eligible to work in India on an ongoing basis