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 processCoach 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).