Senior MLOPS Engineer

Recruiting calibration

This is an AWS-focused MLOps enablement role , not a traditional Data Scientist opening. The person's first objective is to take an existing batch-inference model and establish a secure, repeatable, automated production-deployment framework .

The ideal candidate combines:

Machine learning engineering + AWS SageMaker + DevOps automation + stakeholder enablement

They must be capable of building the platform and teaching Data Scientists how to use it.

Target candidates should demonstrate:

• 3 5+ years in Machine Learning Engineering or MLOps

• Production AWS SageMaker ownership

• Automated model deployment and CI/CD

• Strong Python and SQL

• Infrastructure as Code, preferably Terraform

• Docker and containerized ML workloads

• Experience moving models beyond notebooks into controlled production pipelines

• Ability to train, mentor, and influence Data Scientists

• Exposure to regulated environments

• Familiarity with model governance and risk-management controls

Candidates will be evaluated on the 1.1 99.9 CFA scale using this weighting:

Evaluation areaWeightAWS SageMaker and production MLOps 25% CI/CD, Terraform, and deployment automation 20% Python engineering 15% Batch inference and production operations 10% SQL, Snowflake, DB2, and data integration 10% Docker, Kubernetes, Kafka, and real-time architecture 8% Model Registry, MLflow, Feature Store, and advanced MLOps 7% Training, change management, and regulated-environment experience 5%

Full credit requires implementation-level evidence such as:

• SageMaker Processing or Training Jobs

• Batch Transform or batch inference

• Pipelines

• Model Registry

• Endpoint deployment

• Model artifacts and versioning

• IAM roles and permissions

• VPC/networking controls

• CloudWatch monitoring

• ECR/container integration

• S3 model and feature data

• Promotion between development, test, and production

• Automated testing and rollback

A r sum that only lists "SageMaker" or describes using SageMaker notebooks should receive limited credit.

A top candidate would have personally:

• Taken a model from a notebook or model artifact into production

• Built a secure SageMaker batch-deployment pipeline

• Automated infrastructure and deployments using Terraform and CI/CD

• Written meaningful Python deployment or inference code

• Integrated model workflows with Snowflake, DB2, or similar platforms

• Implemented monitoring, testing, approvals, and rollback

• Worked with Docker and AWS container services

• Introduced Model Registry, Feature Store, MLflow, or model-governance capabilities

• Educated Data Scientists on production MLOps standards

• Worked in banking, healthcare, insurance, or another regulated environment

Strong fit

An MLOps or ML Platform Engineer with direct production SageMaker, Python, Terraform, CI/CD, Docker, and model-lifecycle experience.

Viable with verification

An AWS Machine Learning Engineer with strong SageMaker deployment experience but limited Feature Store, MLflow, or regulated-industry exposure.