Sr Machine Learning AI Engineer MLOps Engineer

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About the Team

About the Role

The Peakon MLOps Engineer is responsible for operating hardening and continuously improving the production infrastructure that powers the Peakon Agent multi-agent architectures AI Features and related ML workloads. This ownership spans deployments monitoring and on-call workflows. Key operational functions include managing the entire deployment lifecycle for the Peakon Agent and other AI Features ensuring the reliability of long-running agentic loops memory stores and tool-use environments while contributing to driving operational excellence across the ML platform. Furthermore the role involves building and maintaining tooling to surface agent trajectory and evaluation data supporting performance testing latency benchmarking and load simulations specific to LLM-driven applications and collaborating on the automation of essential security upgrades for ML dependencies.

About You

Basic Qualifications Summary

• Minimum 8 years of relevant industry experience. Holds a Bachelors/Master or PhD in Computer Science Data Science Statistics Mathematics Engineering or equivalent practical experience.

• Proven track record as an MLOps or ML-savvy SRE/Platform Engineer supporting production-grade LLM and agentic systems. Proficient in Python and frameworks likeLangChainandLlamaIndex.

• Hands-on experience operating containerized services (Docker Kubernetes) using Git-based workflows (GitOps GitHub Actions). Solid understanding of modern ML stacks (platforms feature stores registries messaging layers) or deep platform engineering background.

• Demonstrated ability to own production infrastructure end-to-endmanaging monitoring incident response rollbacks and continuous reliability/uptime improvements.

• Deep understanding of the model development lifecycle specifically regarding model monitoring regression tracking and automated evaluation using tools likeLangSmith.

• Strong communication and collaboration skills under pressure acting as a bridge between ML engineers backend teams and central platform/security specialists.

Other Qualifications Summary

• Solid knowledge of data science principles and ML algorithms applied directly to LLMs RAG and autonomous decision-making agents.

• Experience leading model-building processes including advanced fine-tuning alignment techniques prompt engineering and simulations of agent behaviors.

• Strong understanding of software development principles coupled with demonstrated proficiency in System Design and Architectural Governance to deploy scale and maintain high-availability ML models.

• Expertise in threat modeling and security for ML/agent systems to enforce strict behavioral guardrails. Proven experience navigating highly regulated enterprise environments to ensure data auditability clear ownership boundaries and strict compliance.

• Track record of strong technical decision quality and leadership with the ability to coordinate cross-functional initiatives translate complex business needs into resilient implementations and mentor team members while bridging the gap between ML engineering and platform teams.

Workday Pay Transparency Statement (For EU Locations Only)

Listed below is the base salary range applicable to this position. Workday pay ranges (and the precise pay offered to the successful candidate) are based on a number of objective criteria such as relevant experience and skills and educational qualifications level of responsibility demands of the role work location and business need. As a part of the total compensation package this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus as well as annual refresh stock grants awarded by Workday Inc. For more information regarding Workdays comprehensive benefits please click here.

Our Approach to Flexible Work

With Flex Work were combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections maintain a strong community and do their best work. We know that flexibility can take shape in many ways so rather than a number of required days in-office each week we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers prospects and partners (depending on role). This means youll have the freedom to create a flexible schedule that caters to your business team and personal needs while being intentional to make the most of time spent together. Those in our remote home office roles also have the opportunity to come together in our offices for important moments that matter.

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