Full time
Information Technology
Telecommunications
Job Description
Responsibilities
Feature Development & Delivery: Design, implement, and deploy new features and enhancements to ML products, collaborating with Product and ML Research teams to refine requirements.
Technical Ownership and Stewardship: Own existing and new production ML products, ensuring alignment of technical investments with business goals and engineering best practices; oversee end to end reliability and performance.
Contribute to ML Platform: Help evolve the shared ML platform, driving best practices and shared tooling across all products.
Operational Excellence: Maintain and improve automated CI/CD pipelines, testing frameworks, and monitoring/logging; conduct comprehensive code reviews to enforce standards and share knowledge.
Continuous Improvement: Identify and implement opportunities for process, tooling, and system improvements, proactively addressing technical debt and scaling challenges.
Release Management: Oversee pre release testing, coordinate releases, and ensure smooth enablement of new features.
Leadership: Provide technical guidance and support to other engineers and data scientists, mentor and coach teammates, and foster a culture of collaboration, continuous improvement, and knowledge sharing.
Act Like an Owner: Proactively identify and resolve blockers, navigate processes, independently seek information, and collaborate with relevant teams to resolve ambiguities.
Operate with a Strong Sense of Urgency: Consistently prioritize and execute tasks to meet timelines and deliver results.
Qualifications
5+ years as an ML focused software engineer, MLOps engineer, or similar role with hands on production experience.
Proven expertise with ML model deployment, API design, and integration into production environments.
Strong Python programming skills and familiarity with ML/data libraries.
Experience with containerization, orchestration, and AWS cloud services.
Experience building and operating CI/CD pipelines.
Monitoring, troubleshooting, and optimizing production ML systems.
Pre release testing and release management experience.
Demonstrated ability to work independently, navigate ambiguity, and deliver results.
Excellent communication skills and collaboration across engineering and cross functional teams.
Experience with OpenAPI, FastAPI, or similar frameworks.
Bonus: Experience with MLFlow, model versioning, and storage.
Familiarity with Databricks or similar platforms.
Experience supporting high volume, real time data products.
Automated testing and validation frameworks.
Designing and configuring low latency databases for real time features (e.g., DynamoDB).
Experience with Terraform Cloud.
Experience in large companies, mature engineering teams, or highly regulated industries.
AI assisted coding experience (e.g., GitHub Copilot).
Benefits
Impact: Own high profile ML products, directly influencing reliability, scalability, and evolution of critical production systems.
Autonomy: End to end technical ownership, freedom to shape solutions, best practices, and deliver results in a fast paced environment.
Collaboration: Work with a cross functional, high performing team where expertise is valued and contributions make a real difference.