Senior Machine Learning Engineer, Data & Audience Platform Team

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

Job summary

Jobtailor is seeking an experienced ML Engineer to lead end-to-end development of production ML systems in Hyderabad, India. You will own key ML products, design scalable feature pipelines on Databricks, and architect batch and real-time inference pipelines integrated with Snowflake and activation systems.

The role requires strong Python, Databricks, and SQL skills, plus experience with AWS ML services and ML evaluation frameworks.

Qualifications

• 5–8 years of industry ML engineering or applied data science (3+ years with a Ph.D.).
• Deep Python expertise and production-quality software practices for scale.
• Strong Databricks and SQL/Snowflake experience for feature sourcing and model-output delivery.
• Experience with AWS ML services (SageMaker, S3, Lambda).
• Knowledge of ML model evaluation, A/B testing, and statistical inference; experience in recommendations, identity resolution, embeddings/retrieval, or forecasting.
• Proven ability to lead technical decisions and mentor engineers.

Responsibilities

• Lead end-to-end development of production ML systems: data sourcing, feature engineering, model training, evaluation, deployment, and monitoring.
• Own key ML products such as probabilistic identity resolution, single-title affinity, and audience/propensity models.
• Design scalable feature pipelines on Databricks and the WBD feature store, with documented feature contracts, backfill paths, and freshness SLAs.
• Architect batch and near-real-time inference pipelines integrated with Snowflake and activation systems.
• Develop and optimize models across the ML spectrum: gradient boosting, embeddings/two-tower retrieval, neural ranking, probability calibration, and probabilistic/graph-based matching.
• Design rigorous offline and online experiments; define evaluation frameworks appropriate to each use case.
• Contribute to lookalike modeling using 1,000+ first- and third-party features.
• Champion MLOps best practices: model versioning, automated retraining triggers, drift detection, and production monitoring with MLflow.
• Build and maintain robust, reproducible, auditable ML pipelines on Databricks and enforce leakage prevention and training/serving consistency.
• Mentor MLE 2s through code reviews, design discussions, and pairing.

Skills

Python

Databricks

SQL

MLflow

SageMaker

Snowflake

Model deployment

Mentoring

Communication

Software engineering

Education

Bachelor’s or Master’s in CS/Statistics/Engineering

Tools

Databricks

Snowflake

MLflow

SageMaker

AWS Lambda

PySpark

Unity Catalog

Job description

Responsibilities

• Lead end-to-end development of production ML systems: data sourcing, feature engineering, model training, evaluation, deployment, and monitoring.
• Own key ML products such as probabilistic identity resolution, single-title affinity, and audience/propensity models.
• Design scalable feature pipelines on Databricks and the WBD feature store, with documented feature contracts, backfill paths, and freshness SLAs.
• Architect batch and near-real-time inference pipelines integrated with Snowflake and activation systems.
• Develop and optimize models across the ML spectrum: gradient boosting, embedding/two-tower retrieval, neural ranking, probability calibration, and probabilistic/graph-based matching.
• Design rigorous offline and online experiments; define evaluation frameworks appropriate to each use case.
• Contribute to lookalike modeling using 1,000+ first- and third-party features.
• Champion MLOps best practices: model versioning, automated retraining triggers, drift detection, and production monitoring with MLflow.
• Build and maintain robust, reproducible, auditable ML pipelines on Databricks and enforce leakage prevention and training/serving consistency.
• Mentor MLE 2s through code reviews, design discussions, and pairing.

Requirements

• 5–8 years of industry experience in ML engineering or applied data science (3+ years with a Ph.D.)
• Deep Python expertise and production-quality software engineering practices; production experience building and deploying ML at scale (millions+ of users/records)
• Strong proficiency in Databricks (PySpark, Delta Lake, Workflows/DLT, MLflow, Unity Catalog) and solid SQL/Snowflake experience for feature sourcing and model-output delivery
• Experience with AWS ML services (SageMaker, S3, Lambda)
• Strong understanding of ML model evaluation, A/B testing, and statistical inference; knowledge in one or more of recommendations & ranking, identity resolution, embeddings/retrieval, causal/interpretable ML, forecasting, bandits, or optimization
• Demonstrated ability to lead technical decisions and mentor engineers.
• Bachelor’s or Master’s degree in Computer Science, Statistics, Engineering, or a related quantitative field (or equivalent experience)
• Excellent written and verbal communication, with the ability to advocate technical solutions to engineers, scientists, and product stakeholders.