Applied AI/ML Associate

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.


* Observabi...