Machine Learning Engineer Job Burbank area,California USA,IT/Tech

Job Responsibilities / Typical Day in the Role

Enable production deployment of machine learning models

Partner with Data Scientists to prepare development code for production deployment, including refactoring, packaging, standardization, and performance optimization
Build and maintain CI/CD pipelines for model training, validation, and deployment
Support batch and real-time inference workflows using scalable AWS-native services
Develop and maintain model APIs to be integrated into user products

Build and operate core MLOps platform capabilities

Design and implement a centralized model registry to track versions, metadata, lineage, and promotion stages
Build and maintain a feature store to support consistent feature computation for training and inference
Establish standardized ML pipelines for data ingestion, training, evaluation, deployment, and monitoring
Define infrastructure-as-code patterns to provision and manage ML environments reliably

Ensure reliability, monitoring, and governance of ML systems

Implement monitoring for model performance, data drift, and operational health
Establish alerting and rollback strategies for production model failures
Partner with security and platform teams to ensure compliance, access controls, and auditability

Collaborate across product, data, and platform teams

Work closely with Data Scientists to align experimentation workflows with production constraints
Collaborate with data engineers and architects to ensure feature availability, freshness, and quality
Support agile product PODs by communicating API design and delivery for integration into user products

Must Have Skills / Requirements

Experience in MLOps, ML Engineering, or backend software engineering with ML systems

4+ years of experience

Strong experience building and operating ML systems in AWS (e.g., Sage Maker, ECS, Lambda, Step Functions, S3, IAM)

4+ years of experience

Proficiency in Python and experience with ML frameworks and tooling used in production environments

4+ years of experience

Nice to Have Skills / Preferred Requirements

1) None

Soft Skills

Strong understanding of the machine learning lifecycle and how data science workflows translate to production systems

Ability to collaborate effectively with Data Scientists and translate experimental work into robust production solutions

Strong communication skills and comfort working across technical and non-technical stakeholders

Strong experience building and operating ML systems in AWS (e.g., Sage Maker, ECS, Lambda, Step Functions, S3, IAM)

Proficiency in Python and experience with ML frameworks and tooling used in production environments

Experience building APIs and backend services for model inference

Hands-on experience with CI/CD, infrastructure as code (e.g., Terraform, Cloud Formation), and containerization

Education / Certifications

None required.

Equal Opportunity Employer Statement

Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.

Contact

If you are inclined, I would be happy to set up some time to chat more about your background and career interests to see if there could be a possible match. Please feel free to call me on or send me email on

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