Role Overview - ML Platform Lead

We are seeking an experienced Machine Learning &

- AI Platform Lead to spearhead our AI initiatives, manage end-to-end deployments, and drive our technical strategy. In this dual-capacity role, you will guide our teams in model operationalization (MLOps) while owning the Token Platform and AI Gateway. The ideal candidate brings a deep technical background in ML engineering and cloud-native services, coupled with strong leadership skills to align our AI adoption roadmap with overarching business goals.

Key Responsibilities

Leadership &

- Strategy

- Ensure all AI/ML solutions align with business objectives, performance metrics, and compliance requirements.

- Collaborate with cross-functional teams to shape data strategy, data governance, and the organization's AI adoption roadmap.

- Manage and lead the Token Platform and AI Gateway.

- Guide and mentor engineering teams in model operationalization (MLOps), model versioning, and building robust retraining pipelines.

Core Engineering &

- Model Development

- Evaluate and select the most appropriate ML algorithms, frameworks, and cloud platforms for business needs.

- Build and optimize batch jobs for heavy data processing and model training.





- Oversee Generative AI evaluations and lead the implementation of content filters.

- Conduct thorough code reviews to maintain the highest standards of code quality and performance.

- Own and manage end-to-end ML and platform deployments.

Technical Requirements

- Languages &
- Frameworks:

High proficiency in Python, modern ML libraries, and cloud-native development.

- Cloud Platforms: Deep expertise in Azure (Azure Machine Learning Studio, Azure AI Foundry, Functions App, Cosmos DB, Azure DevOps, Key Vault) OR equivalent experience in AWS or GCP.

- Data &

- AI Platforms:

Hands-on experience with Snowflake AI services, specifically Cortex AI and Snowpark Container Services (SPCS).

- Scale &

- Performance:

Proven track record of large-scale model deployment, infrastructure management, and continuous performance tuning.

Qualifications &

- Experience

- Experience: 10+ years of solid, hands-on experience in Machine Learning Engineering and MLOps.

- Bonus/Preferred: A solid background in platform engineering or platform operations is considered a strong added advantage.

📌 ML Architect (Delhi)
🏢 Tiger Analytics
📍 Delhi