Staff Machine Learning Engineer, Data & Audience Platform

Jobtailor; Hyderabad; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Jobtailor is seeking a senior ML engineer to own the technical architecture for core ML systems, including probabilistic identity and audience intelligence platforms. The role requires steering MLOps, feature stores, and model governance on a Databricks-first setup with Snowflake and SageMaker.

You will lead the design of scalable ML pipelines, production monitoring, and large-scale model serving while mentoring teams and aligning with executive stakeholders.

Qualifications

• 8+ years of ML engineering experience, with at least 6 years after PhD.
• Full-stack ML expertise: data engineering, feature engineering, model development, MLOps, and production monitoring.
• Strong Databricks expertise: Delta Lake, Unity Catalog, Workflows/DLT, MLflow, Feature Store, Asset Bundles, Genie Space configuration.
• Very strong AWS (SageMaker, S3, Lambda, Glue) and Snowflake (DCR, Snowpark, Cortex) proficiency, scalable at large user bases.

Responsibilities

• Define and own the technical architecture for core systems including identity spine, audience intelligence, content-affinity and forecasting models.
• Lead MLOps framework decisions—feature-store design, training pipelines, model serving, monitoring—on a Databricks-first architecture with Snowflake and AWS SageMaker integration.
• Architect and lead delivery of the probabilistic identity resolution system at scale across all brands, using entity resolution and representation learning.
• Define the team's MLOps target architecture: feature contracts, model registry governance, retraining, drift detection, and A/B infrastructure.
• Lead adoption of agentic AI development standards (Cursor, Copilot, Amazon Q) in production ML workflows.

Skills

ML engineering

Production monitoring

Databricks

SageMaker

Snowflake

Leadership

Education

PhD in ML / CS

Tools

Databricks

Delta Lake

Unity Catalog

MLflow

Genie Space

Cortex

SageMaker

Job description

Responsibilities

• Define and own the technical architecture for the team’s core systems: the probabilistic identity spine, audience intelligence platform, content-affinity and genre-preference models, and ML-based forecasting
• Lead architectural decisions for the team’s MLOps framework—feature-store design, training-pipeline standards, model-serving patterns, and monitoring infrastructure—on a Databricks-first architecture, integrating Snowflake and AWS SageMaker where each is the right tool
• Architect and lead delivery of the probabilistic identity resolution system—resolving unauthenticated device IDs and 1P cookies to households/persons with calibrated confidence at scale across all WBD brands—using entity resolution, embeddings/representation learning, calibration, candidate blocking, and champion/challenger promotion
• Define the team’s MLOps target architecture: feature contracts, model-registry governance, automated retraining, drift detection, and A/B experimentation infrastructure
• Lead the team’s adoption of agentic AI development: define standards for using Cursor, GitHub Copilot, and Amazon Q in production ML workflows, and for MCP-based tooling

Requirements

• 8+ years of industry experience in ML engineering (6+ with a Ph.D.)
• Mastery of the full ML stack: data engineering, feature engineering, model development, MLOps, and production monitoring
• Deep Databricks expertise: Delta Lake, Unity Catalog, Workflows/DLT, MLflow, Feature Store, Asset Bundles, and Genie Space configuration
• Strong AWS proficiency (SageMaker training/pipelines/model registry, S3, Lambda, Glue) and Snowflake expertise (DCR patterns, Snowpark, Cortex, SQL optimization at scale)
• Proven experience architecting production ML systems serving millions of users, and a track record of technical leadership (setting standards, driving architecture, influencing across teams)
• Expert proficiency with ML frameworks (PyTorch, TensorFlow, XGBoost/LightGBM, scikit-learn) and deep understanding of statistics and ML fundamentals
• Excellent communication, with the ability to advocate technical solutions to engineering, science, product, and executive audiences.