AI​/ML Data Engineer

We are looking for an AI/ML Data Engineer to work for our client. The ideal candidate aligns with the responsibilities and qualifications outlined below.

Our client is seeking an AI/ML Data Engineer to design, build, and scale modern data and machine learning platforms. You’ll partner with data scientists and product/engineering teams to develop reliable data pipelines, product ionize ML models, and enable robust MLOps practices—driving measurable business impact from data and AI.

• Design and build cloud-scale data pipelines (batch & streaming) using tools such as Spark/Databricks, Airflow/Prefect, Kafka/Kinesis, and DBT
• Develop feature engineering workflows, feature stores, and reusable data assets for ML
• Productionize models with ML pipelines (training, evaluation, deployment) using MLflow/Sage Maker/Vertex AI/Azure ML
• Implement MLOps best practices: CI/CD for data & ML, automated testing, model registry, canary/blue‑green deployments
• Build monitoring for data quality, model performance (drift, bias, accuracy), and platform health
• Optimize data storage and compute performance (partitioning, Z‑ordering, indexing, caching)
• Enforce data governance & security (IAM, secrets management, lineage, PII handling)
• Collaborate with data scientists, analysts, and software engineers to translate requirements into scalable solutions
• Create documentation and provide enablement for stakeholders and downstream consumers

• 5+ years of experience in data engineering or ML engineering, including production systems
• Strong skills in Python and SQL; experience with Spark (PySpark) and Databricks or similar platforms
• Hands-on experience with one major cloud (AWS, Azure, or GCP) and services for data & ML (e.g., S3/ADLS/GCS, EMR/Databricks, Sage Maker/Azure ML/Vertex AI)
• Workflow orchestration (e.g., Airflow, Prefect, Dagster) and CI/CD (Git Hub Actions, Azure Dev Ops, Git Lab CI)
• Containerization and deployment with Docker;
  Kubernetes experience is a plus
• Streaming data experience (e.g., Kafka, Kinesis, Pub/Sub) strongly preferred
• Knowledge of MLOps frameworks (MLflow, model registries), testing (unit/integration), and observability (metrics, logging)
• Familiarity with data quality frameworks (Great Expectations, Deequ) and governance (Lakehouse/medallion architecture, lineage)

• Competitive compensation with performance bonus
• Modern data stack (Databricks/Spark, Airflow/Prefect, MLflow) and green‑field build opportunities
• High visibility to leadership and ownership over ML platform decisions
• Professional development budget (certifications, conferences, courses)