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.