Senior Level Mlops Engineer Jobs
Senior level mlops engineer jobs place experienced engineers in charge of platform architecture, model deployment pipelines, and the cross-functional teams that keep production systems running reliably at scale. Roles are concentrated in Technology & Software and Human Resources, with 0% remote or hybrid, and employers like TRM Labs, 6sense, and TriNet hiring at this level now.
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Our Mission: 6sense's mission is to multiply what matters: growth, retention, and efficiency. We envision a future where companies, teams and people reach their full potential.
Our People: People are the heart and soul of 6sense. We serve with passion and purpose. We live by our Being 6sense values of Win as One Team, Stay Curious, Do The Right Thing, Own the Outcome, and Create Belonging. Every 6sensor plays a part in defining the future of our industry-leading technology. 6sense is a place where difference-makers roll up their sleeves, take risks, act with integrity, and measure success by the value we create for our customers. We want 6sense to be the best chapter of your career.
Senior MLOps Engineer, Forward Deployed
TEAM Forward Deployed Engineering (FDE), Predictive track, in Data Science & AI Engineering
REPORTS TO MLOps engineering lead
LEVEL Senior (6+ years)
LOCATION United States
THE ROLE IN ONE PARAGRAPH This is a hands-on MLOps engineering role. You will work across model training and refresh workflows, APIs, orchestration, data pipelines, production monitoring, and user-facing experiences such as model dashboards and training controls. When a customer needs a better model, a refresh workflow, or visibility into model health, you will help make it real: build the experience, connect it to reliable backend systems, validate the outcome, and operate it safely in production. You'll be our first MLOps engineer in the US, working closely with the MLOps team in India so customer work reaches production in days, not weeks. Most of your work is behind the scenes, with customer conversations when the technical details matter.
Build for the customer
- Productionize customer-tailored predictive models, configurations, and scoring workflows with validation, release, monitoring, and rollback paths.
- Build and improve UI workflows for training triggers, model refreshes, validation outcomes, model status, and operational troubleshooting.
- Enhance model dashboards that make model health, score changes, data quality, refresh status, and production issues understandable to internal users and customers.
- Build or modify the APIs, backend services, data contracts, and orchestration workflows behind those experiences.
- Automate model retraining, validation, promotion, and deployment workflows for customer engagements.
- Create safeguards for data quality, score distributions, threshold changes, and edge cases before model changes reach production.
- Monitor models and services for drift, data shifts, reliability issues, and operational regressions.
Productize for everyone
- Turn repeatable customer solutions into reusable product and platform capabilities with Product, Data Science, and Platform Engineering.
- Build deployment templates, reusable workflow components, and configuration-driven interfaces that support many customers without one-off operational work.
- Create self-service or assisted tools for teams to initiate training, monitor refreshes, review validation results, and troubleshoot model issues safely.
- Improve the MLOps platform where needed, including Kubernetes workloads, compute, pipelines, CI/CD, deployment tooling, and observability.
- Establish clear runbooks, release checklists, audit trails, and support processes for model operations.
- Identify recurring customer pain points and convert them into product requirements, platform improvements, or durable operational tooling.
What You'll Bring
- 6+ years of experience in MLOps, ML platform engineering, backend engineering, full-stack engineering, or related production engineering roles.
- Experience owning machine-learning systems in production, including training, deployment, monitoring, retraining, and incident response.
- Strong Python and SQL skills, including APIs, backend services, asynchronous workflows, testing, CI/CD, and observability.
- Experience building or materially contributing to internal tools, operational dashboards, or customer-facing product workflows.
- Comfortable debugging across the stack: UI, APIs, workflows, data pipelines, model artifacts, and production infrastructure.
- Experience releasing changes safely across multiple customers or production environments, including staged rollout, impact checks, and rollback.
- Working knowledge of Kubernetes and cloud infrastructure.
- Strong communication and judgment when balancing customer urgency, usability, reliability, and long-term maintainability.
- Nice to have: Fast API and Frontend experience.
- Airflow, Spark, MLflow, model registries, feature pipelines, or workflow orchestration.
- AWS, S3, Hive, distributed compute, and multi-tenant SaaS experience.
- Forward-deployed, consulting, solutions, implementation, or customer-facing engineering experience.
- B2B SaaS, go-to-market data, or predictive modeling experience.
What Success Looks Like
Within 3 months
- Understand the predictive-model lifecycle and improve at least one meaningful deployment, support, or visibility bottleneck.
- Bring clear release, monitoring, and support plans to active customer model engagements.
Within 6 months
- Common training, refresh, validation, and deployment workflows are materially more automated.
- Model dashboards or internal workflows provide clear visibility into model status, health, and refresh progress.
- Customer model changes move to production faster, with stronger operational controls.
Within 12 months
- At least one customer-specific solution has become a reusable product or platform capability.
- The team can reliably deliver and operate customer-tailored models without fragile manual processes or overnight handoffs.
Why this role is different: You won’t only maintain ML infrastructure. You’ll build the product experiences and production systems that make predictive models easier to launch, understand, refresh, and trust—then turn what works for one customer into something that benefits many.
Base Salary Range: $191,475.00 - $260,830.00. The base salary range represents the anticipated low and high end of the base salary range for this position. Actual salaries may vary and may be above or below the range based on various factors, including but not limited to work location and experience. The base salary is one component of 6sense’s total compensation package for this position. Other compensation may include a bonus program or commission plan, and stock options if approved by 6sense’s board. In addition, 6sense provides a variety of benefits, including generous health insurance coverage, life, and disability insurance, a 401K employer matching program, paid holidays, self-care days, and paid time off (PTO).
Our Benefits: Full-time employees can take advantage of health coverage, paid parental leave, generous paid time-off and holidays, quarterly self-care days off, and stock options. We’ll make sure you have the equipment and support you need to work and connect with your teams, at home or in one of our offices. We have a growth mindset culture that is represented in all that we do, from onboarding through to numerous learning and development initiatives including access to our LinkedIn Learning platform. Employee well-being is also top of mind for us. We host quarterly wellness education sessions to encourage self-care and personal growth. From wellness days to ERG-hosted events, we celebrate and energize all 6sense employees and their backgrounds.
Equal Opportunity Employer: 6sense is an Equal Employment Opportunity and Affirmative Action Employers. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status. If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to jobs@6sense.com.
We are aware of recruiting impersonation attempts that are not affiliated with 6sense in any way. All email communications from 6sense will originate from the @6sense.com domain. We will not initially contact you via text message and will never request payments. If you are uncertain whether you have been contacted by an official 6sense employee, reach out to jobs@6sense.com.
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- Technology & Software
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Senior Level Mlops Engineer Jobs: Frequently Asked Questions
How do I get a senior level mlops engineer job?
Senior level roles go to engineers who can demonstrate end-to-end ownership of ML infrastructure, not just execution of assigned tasks. Employers look for candidates who have designed and scaled model deployment pipelines, led incident response, and mentored junior engineers. Strong candidates can speak to architectural trade-offs they drove, production failures they resolved, and measurable reliability or latency improvements they delivered.
Which companies hire senior level mlops engineers?
Companies hiring senior level mlops engineers right now include TRM Labs, 6sense, and TriNet, based on current listings on Migrate Mate as of November 2026. Hiring at this level tends to come from organizations running large-scale ML systems in production, including technology platforms, financial services firms, and data-intensive enterprises that need experienced engineers to own infrastructure reliability and platform direction.
Are there remote senior level mlops engineer jobs?
Yes, remote availability is strong at the senior level. About 0% of senior level mlops engineer openings are remote or hybrid as of November 2026, reflecting how many engineering organizations hire distributed senior talent rather than requiring on-site presence. Fully remote roles at this level typically still involve regular cross-functional collaboration, so async communication and documentation skills carry real weight.
What makes a mlops engineer role senior level?
Senior level mlops engineers own outcomes, not just deliverables. That means setting the technical direction for model deployment or observability infrastructure, making architectural decisions with long-term reliability implications, and holding accountability when production systems degrade. Senior engineers are also expected to mentor mid-level colleagues, drive standards across the team, and contribute to roadmap planning alongside product and data science stakeholders.
Which industries hire the most senior level mlops engineers?
Senior level mlops engineer roles concentrate in Technology & Software and Human Resources, based on current listings on Migrate Mate as of November 2026. These sectors drive hiring at this level because they operate ML systems at a scale and criticality that demands experienced engineers who can build for reliability, govern model behavior in production, and lead the infrastructure work without close oversight.