Lead ML Engineer

Role summary

Own the end-to-end ML lifecycle—from clarifying business goals and designing data/ML architectures to building, deploying, and monitoring models that drive measurable impact. You’re hands-on in Python and comfortable across modern data platforms and ML stacks (pick the right tool for the job), with strong judgment about when to favor classic ML vs. deep learning and how to productionize responsibly at scale.

What you’ll do

• Partner with the business to translate requirements into clear problem statements, KPIs, and experiment plans (A/B, holdout, backtests).

• Design data & ML architectures on lakehouse/warehouse stacks (e.g., Oracle Exadata, Spark/Databricks;  Snowflake/BigQuery/Redshift with open table formats like Iceberg/Delta/Hudi or equivalent ).

• Build pipelines for ingestion, feature engineering, and training (batch & streaming) using Python + SQL with orchestration (Airflow/Prefect/Dagster).

• Model using scikit-learn/XGBoost/LightGBM and  PyTorch/TensorFlow; manage experiments and lineage

• Serve & operate models on a major cloud ML platform (Azure ML, SageMaker or Vertex AI), with CI/CD, canary/blue-green, and rollback guardrails.

• Monitor & improve: implement data/model quality and drift monitoring, alerting, and dashboards; close the loop with BI (Power BI/Tableau/Looker).

• Document & review: author concise design docs and run technical reviews; mentor engineers; champion responsible AI practices.

Must-have experience

• 8+ years in applied ML & data engineering (3+ years leading delivery of production ML systems).

• Python expert with production-grade SQL; strong with pandas/Polars, scikit-learn, and one of: XGBoost/LightGBM.

• Fluency in core ML toolkits including TensorFlow, PyTorch, scikit-learn, and familiarity with Hugging Face or equivalent frameworks.

• Proven record of constructing and maintainingscalable data pipelines—both batch and streaming—for model training and deployment.

• Data platforms: hands-on with one of: Oracle ExaData, Spark/Databricks, or Snowflake, BigQuery/Redshift or equivalent; comfortable with open table formats (Iceberg/Delta/Hudi).

• Orchestration: real projects using one of Airflow, Prefect, or Dagster.

• Cloud ML platform: production deployments on one of SageMaker, Vertex AI, or Azure ML (pipelines, endpoints, registries).

• MLOps: CI/CD for ML, experiment tracking, model registry, observability (latency, errors), and data/model drift monitoring.

• Communication: ability to frame trade-offs and influence cross-functional partners; crisp writing of design/decision docs.

Nice-to-have

• Feature stores for training/serving consistency and reuse.

• Streaming (Kafka/Event Hubs/PubSub) and time-series/forecasting at scale.

• GenAI (prompt/eval/fine-tuning) when it adds clear value.

• BI & semantics (Power BI/Tableau/Looker models) to translate model output into decisions.

Why join our AI CoE?

You'll design and lead the intelligent fabric behind our AI solutions—merging data engineering finesse with generative creativity to deliver scalable, insightful, and responsible models. If you're passionate about spearheading next-generation AI systems, mentoring talented engineers, and shaping organizational culture, this role is your opportunity to make a lasting impact.