Bright. AI
Physical AI
StaffMLOpsEngineer–MLPlatform
“Staff MLOps Engineer – ML Platform at Bright. AI. Skills: MLOps, ML platform development, AWS, CI/CD for ML, observability, governance, data pipelines, model deployment. Design, build, and operate our ML/AI development platform on AWS. Build automated data pipelines”
What You'll Achieve.
enable intelligent decision-making at scale; move from notebook to secure, reliable, and cost-efficient production services quickly; turn ideas into durable, monitored ML services
Industry & Context.
intelligent decision-making at scale; optimize for cost/performance
What They're Looking For.
Must Have
applied experience building production grade ML platforms, 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)
Nice to Have
Distributed training at scale (SageMaker Training, PyTorch DDP, Hugging Face on SageMaker), Data engineering at scale (e. g. , SparkR, Glue, Redshift), Observability stacks (e. g. , Grafana), performance tuning, and capacity planning for ML services, LLMOps/RAG (Bedrock, vector databases, evals), Prior startup experience building ML platforms and products from the ground up
What You'll Do.
and operate our ML/AI development platform on AWS
Build automated data pipelines
Stand up experiment tracking and a model registry
Implement CI/CD for ML
Ship real‑time endpoints and batch endpoints
Build monitoring service telemetry
support RAG pipelines with vector stores (OpenSearch) and evaluation harnesses
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