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BrightAI · 1 week ago

Sr. MLOps Engineer – ML Platform

Palo Alto, CA

Full-time

Onsite

Senior Level

5+ years exp

Bright.AI is a high-growth Physical AI company transforming how infrastructure businesses interact with the physical world through intelligent automation. They are seeking a Senior MLOps Engineer to lead the build-out of their cloud-native ML developer platform and production pipelines, focusing on designing scalable data/model workflows and CI/CD for ML. This role is pivotal in enabling teams to transition from notebook to secure, reliable, and cost-efficient production services quickly.

Artificial Intelligence (AI)Cloud ComputingInternet of ThingsMobile

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

Qualification

MLOpsAWSPythonCI/CD for MLDockerTerraformData engineeringExperiment trackingMonitoring & observabilitySoft skills

Required

B.S. or M.S. in Computer Science, Electrical/Computer Engineering, or related field; advanced degree a plus

5+ years in software/ML engineering, including 2+ 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)