Location: Houston, TX, United States

Join and be a part of ML Engineer/Software Eng. to design, build, deploy, and operate production-grade ML Applications.

 

As an ML Engineer/Software Engineer within the team, you will design, build, deploy, and operate production-grade machine learning services. You will partner closely with Data Scientists to industrialize models, build Python-based model wrappers, manage Kubernetes-based runtime (GKP) in UAT and Production, and ensure reliable CI/CD for model hosting packages. This role also coordination with other technology team, task planning, and mentoring team members.

Job Responsibilities:

• Build robust Python model wrappers and service interfaces that standardize inference, logging, and telemetry for multiple ML models.
• Develop and maintain ML pipelines for packaging, testing, and deployment across UAT and Production, including versioning and rollback strategies.
• Operate and maintain GKP Kubernetes pods and scheduled jobs in UAT and Production, including configuration, scaling, resource quotas, secrets, and monitoring.
• Own model host package builds and deployments through CI/CD, including promotion workflows, environment configuration, and change management.
• Troubleshoot and resolve UAT and Production issues end-to-end (application, model, data, infrastructure), performing root-cause analysis and implementing fixes, including model updates as needed.
• Partner with Data Scientists during model development to integrate feature code, create reusable Python libraries, write unit tests, perform code reviews, and improve reproducibility.
• Engineer performant data access for model inference and batch jobs against Oracle Database (SQL optimization, schemas, stored procedures) and support data pipeline needs.
• Establish and enforce operational excellence practices: health checks, observability, alerting, SLOs/SLAs, security baselines, and documentation.
• Participate in scrum ceremonies and the agile process
• Coach and assist team members to remove blockers, share best practices, and elevate code quality and delivery efficiency.

 

Required qualifications, capabilities, and skills   

• Strong Python software engineering skills (OOP, packaging, dependency management, virtual environments) and testing (pytest, mocking, coverage).
• Hands-on Kubernetes cloud based experience operating services in UAT/Production (deployments, pods, jobs/autosys, liveness/readiness probes, autoscaling, logs, and metrics).
• Experience building and releasing production ML services (model packaging, API serving, model/version management).
• CI/CD experience (e.g., Jenkins, GitLab CI, GitHub Actions, Spinnaker) for automated builds, tests, security scans, and multi-environment deployments.
• Proficiency with Oracle SQL and performance tuning for model-serving and batch workloads.
• Solid Linux fundamentals, shell scripting, Git workflows, and code review practices.
• Observability and operational ownership with experience responding to and preventing production incidents.
• Excellent communication and collaboration skills, with a track record of working alongside Data Scientists and coordinating multi-team projects.
• Demonstrated ability to plan own work, manage one's schedule, and drive one's execution across concurrent initiatives.

 

Preferred qualifications, capabilities, and skills 

• MLOps tooling experience (e.g., MLflow, Airflow, Kubeflow, feature stores, model registries, drift monitoring)
• Data engineering at scale (Spark/Databricks), streaming (Kafka), and batch orchestration.
• Infrastructure-as-Code (Terraform), artifact and container management, and container hardening.
• Familiarity with model governance, validation, and audit requirements in regulated environments.
• Bachelor’s or Master’s in Computer Science, Engineering, or related field (or equivalent experience).

 

AI/ML Engineering Associate: Hands-on ML Engineer/Software Eng. to design, build, deploy, and operate production-grade ML Applications