Senior ML Ops Engineer

Role Description

As a Senior ML Ops Engineer, you will:

• Design, build, and operate production-grade ML platforms and infrastructure across AWS and Azure in a globally distributed environment.
• Bridge the gap between research prototypes and production systems by improving software quality, deployment workflows, automation, and operational processes.
• Contribute to the technical architecture by identifying production risks, proposing pragmatic alternatives, and improving system reliability and scalability.
• Improve engineering transparency through architectural decision records, dependency mapping, operational documentation, and post-incident reviews.
• Mentor engineers across the team, promote engineering best practices, and help cultivate a culture of technical excellence and continuous improvement.

Qualifications

• 5+ years of experience in software engineering, machine learning engineering, or a related field, including at least 2 years of building and operating production ML or other data-intensive systems.
• Demonstrated ownership of cloud infrastructure and deployment pipelines, including Infrastructure as Code, AWS or Azure, CI/CD, and containerized applications.
• Strong Python programming skills and hands-on experience with modern data and ML platforms such as Databricks, Apache Spark, Delta Lake, and MLflow.
• A production-first engineering mindset, with experience in testing, observability, debugging, performance optimization, reliability engineering, and delivering enterprise ML systems that meet operational SLAs.
• Strong system design and communication skills, with the ability to define clean interfaces, evaluate architectural trade-offs, and make sound technical decisions in ambiguous environments.
• Experience working in international, cross-functional organizations, supporting the full product lifecycle from pre-sales demonstrations through production deployment for global enterprise customers.

Requirements

• Experience with ML Ops platforms, model lifecycle management, feature stores, model monitoring, or AI evaluation infrastructure.
• Experience deploying and operating LLM-based or agentic AI systems in production.
• Familiarity with engineering maturity frameworks or organizational models such as CMMI, Cynefin, or similar approaches.
• Experience working through periods of significant organizational or technical change, including mergers, platform migrations, or large-scale reorganizations.

What This Role Is Not

This is not a narrowly scoped implementation or ticket-driven engineering role. We are looking for an engineer who can work across software, infrastructure, ML systems, and engineering processes, helping shape technical direction while remaining hands-on. Success in this role requires sound technical judgment, ownership, and the ability to balance immediate delivery with long-term maintainability.

Benefits

Centric Software provides equal employment opportunities to all qualified applicants without regard to race, sex, sexual orientation, gender identity, national origin, color, age, religion, protected veteran or disability status or genetic information.