Architect - Machine Learning (MLOps Specialist)

Must have skills & Qualifications

• 8+ years working in ML/AI engineering or MLOps roles with strong architecture exposure.
• Strong expertise in AWS cloud-native ML stack , including: EKS (primary), ECS, Lambda, API Gateway, CI/CD (CodeBuild/CodePipeline or equivalent)
• Hands-on experience with at least one major MLOps toolset and awareness of alternatives: MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, Seldon
• Deep understanding of model lifecycle management (training → registry → deployment → monitoring).
• Experience implementing or supporting LLMOps pipelines , including: prompt versioning, evaluation metrics, automation frameworks
• Deep understanding of ML lifecycle : data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance.
• Strong experience with AWS SageMaker (Training, Processing, Batch Transform, Pipelines, Feature Store, Model Registry, Model Monitor).
• Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
• Ability to build dynamic and versioned pipelines using SageMaker Pipelines, Step Functions, or Kubeflow.
• Strong SQL and data transformation experience using Snowflake , Databricks, Spark, or EMR.
• Experience with feature engineering pipelines and Feature Store management (SageMaker or Feast).
• Understanding of lineage tracking : training data snapshot, feature versions, code versioning, metadata tracking, reproducibility.
• Hands-on experience with Bedrock , OpenAI , Anthropic , or Llama models.
• Experience with CloudWatch , SageMaker Model Monitor , Prometheus/Grafana , or Datadog .
• Strong foundation in Python and cloud-native development patterns.
• Solid understanding of security best practices, IAM, secrets management, and artifact governance.

Good to have skills