AI/ML Engineer

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.