We are now hiring a Staff MLOps Engineer to lead the build-out of our cloud-native ML developer platform and production pipelines. This role is pivotal to building an integrated ML/AI development platform with programmatic data analysis and algorithm development capability on AWS-so teams can move from notebook to secure, reliable, and cost-efficient production services quickly.

You'll work at the intersection of ML engineering, cloud infrastructure, and developer experience, designing scalable data/model workflows, CI/CD for ML, observability, and governance that turn ideas into durable, monitored ML services.

Key Responsibilities:
• Design, build, and operate our ML/AI development platform on AWS-including Amazon SageMaker AI (Studio/Notebooks, Training/Processing/Batch Transform, Real-Time & Async Inference, Pipelines, Feature Store) and supporting services.
• Establish golden-path project templates, base Docker images, and internal Python libraries to standardize experiments, data processing, training, and deployment workflows.
• Implement Infrastructure-as-Code (e.g., Terraform) and workflow orchestration (Step Functions, Airflow); optionally support EKS for training/inference.
• Build automated data pipelines with S3, Glue, EMR/Spark (PySpark), Athena/Redshift; add data quality (Great Expectations/Deequ) and lineage.
• Stand up experiment tracking and a model registry (SageMaker Experiments & Model Registry or MLflow); enforce versioning for data, code, and models.
• Implement CI/CD for ML (CodeBuild/CodePipeline or GitHub Actions): unit/integration tests, data contracts, model tests, canary/shadow deployments, and safe rollback.
• Ship real-time endpoints (SageMaker endpoints/FastAPI on Lambda/ECS/EKS) and batch jobs; set SLOs and autoscaling, and optimize for cost/performance.
• Build monitoring & observability for production models and services (drift, performance, bias with SageMaker Model Monitor; service telemetry with CloudWatch/Prometheus/Grafana).
• Enforce security & governance: least-privilege IAM, VPC isolation/PrivateLink, encryption, secret management.
• Partner with backend engineers to productionize notebooks and prototypes.
• Help integrate GenAI/Bedrock services where appropriate; support RAG pipelines with vector stores (OpenSearch) and evaluation harnesses.

Educational Background
• B.S. or M.S. in Computer Science, Electrical/Computer Engineering, or related field; advanced degree a plus.
• Strong foundation in machine learning systems, distributed computing, and data engineering; applied experience building production grade ML platforms.

Required Skills & Expertise
• 8+ years in software/ML engineering, including 4+ years in MLOps or in a similar role.
• Strong programming skills (proficient in Python), fluent with Docker and Terraform or AWS CDK.
• Hands-on with AWS: SageMaker, S3, IAM, CloudWatch, ECR, and ECS/EKS/Lambda.
• Built and operated CI/CD for ML (tests for code/data/models; automated deploys) and shipped real-time & batch ML workloads to production.
• Experience with experiment tracking & model registry (e.g., SageMaker Experiments/Model Registry or MLflow) and data versioning.
• Implemented monitoring & quality (SageMaker Model Monitor, EvidentlyAI, Great Expectations/Deequ) and created on-call/runbooks for model & service incidents.
• Solid grasp of security & compliance in cloud ML (IAM policy design, VPC/private networking, KMS encryption, secrets management, audit logging).

Bonus Qualifications
• Distributed training at scale (SageMaker Training, PyTorch DDP, Hugging Face on SageMaker).
• Data engineering at scale (e.g., Spark/EMR, Glue, Redshift).
• Observability stacks (e.g., Grafana), performance tuning, and capacity planning for ML services.
• LLMOps/RAG (Bedrock, vector databases, evals) as optional capabilities.
• Prior startup experience building ML platforms and products from the ground up.