About the role
We are looking for Senior AI/ML Ops Engineers who crave variety and ownership. You'll have the opportunity to work across multiple teams and disciplines, building a versatile skill set while solving the complex challenges of a global platform.
Responsibilities:
- Design, develop, and maintain 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 optimise infrastructure for training and serving, utilising Docker, Kubernetes, or serverless platforms
- Monitoring and 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 Optimisation: Manage GPU/CPU utilisation to minimise 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 and Governance: Manage versioning for data, code, and models using tools like MLflow.
- Security and 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.
Requirements:
- 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 the Databricks Lakehouse ecosystem.
- AI/MLOps workflows on Databricks, MLflow, Mosaic AI Agent Framework, Unity Catalogue, 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 an 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.