About this role
Job description
Key responsibilities
- Design and maintain end-to-end ML pipelines: ingestion, feature engineering, training, validation, deployment, and retraining.
- Build and run feature stores and training datasets with versioning, lineage, and reproducibility.
- Implement CI/CD/CT for models: automated testing, model registry, approval gates, and staged rollouts (shadow, canary, A/B).
- Deploy models for batch and real-time inference on containerized, autoscaling infrastructure.
- Monitor model performance, data drift, concept drift, latency, and cost, with alerting and retraining triggers.
- Build scalable batch and streaming data pipelines that feed ML workloads, with data quality checks built in.
- Manage infrastructure as code for ML environments across dev, test, and prod.
- Enforce model governance: access control, audit trails, explainability artifacts, and compliance requirements.
- Help data scientists move from notebooks to production-grade, modular code.
- Support GenAI/LLM workloads where applicable: prompt and version management, evaluation pipelines, and vector stores.
Required skills
Area Requirement
Experience 4+ years in data engineering (ETL/ELT, data modeling, orchestration) and 2+ years in MLOps or ML platform work
Languages Strong Python and SQL;
Scala or Java a plus
Area Requirement
Data processing Spark/PySpark and distributed processing at scale
Orchestration Airflow, Kubeflow Pipelines, or similar
ML lifecycle MLflow, Vertex AI, SageMaker, or Azure ML (registry, experiment tracking)
Containers Docker and Kubernetes
CI/CD GitHub Actions, GitLab CI, Jenkins, or Cloud Build
Infrastructure as code Terraform or equivalent
Cloud GCP preferred (BigQuery, Dataflow, Dataproc, GCS, Vertex AI); AWS or Azure equivalents accepted
Observability Prometheus, Grafana, Evidently, or cloud-native tooling
Nice to have
- Streaming: Kafka, Pub/Sub, NiFi, or Flink.
- Feature stores: Feast, Vertex Feature Store, or Tecton.
- Model serving: KServe, Seldon, BentoML, or Triton.
- LLMOps: LangChain or LlamaIndex, vector databases, and LLM evaluation frameworks.
- Experience in telecom, financial services, or other regulated, high-volume environments.
- Certifications: GCP Professional ML Engineer or Data Engineer, AWS ML Specialty.
What success looks like in the first 6 months
- Model deployment time cut from weeks to days through automated pipelines.
- Every production model has versioned data, code, and artifacts, plus drift monitoring.
- A reusable ML pipeline template adopted by at least two data science teams.
Education
Bachelor's in Computer Science, Engineering, or a related field, or equivalent experience.