Senior MLOps Engineer – ML Leadership Role at Fluent

Fluent, LLC; United States; Remote; USD 92,000 - 99,000; Full time

Benefits offered by this job

Health, dental, and vision insurance

Pre-tax savings plans

Transit/parking programs

401K with employer match

Educational and social events

Volunteer opportunities

Job summary

Fluent, Inc. is seeking a Senior MLOps Engineer (remote) based in Canada to co-own MLOps practices, deploy reliable production ML systems, and mentor the data/ML team. You will partner with ML scientists, data engineering, and product to optimize model deployment and cost-to-serve while upholding engineering standards.

You will lead pipelines for training and deployment, design scalable serving infra, and monitor performance and cost, enabling accurate ad selection and scalable ML operations.

Qualifications

• Strong experience with Databricks and AWS-based ML workflows.
• Proficiency with Python, SQL, PySpark, and ML/data engineering tools.
• Experience with ML lifecycle tools such as MLflow and Delta Live Tables.
• Ownership of architecture and operations for real-time, low-latency model serving.
• Hands-on CI/CD, automated testing, and monitoring systems.
• Ability to independently resolve production challenges and collaborate cross-functionally.
• Experience reviewing PRs, setting engineering standards, and mentoring teammates.

Responsibilities

• Collaborate with ML scientists to operationalize models in Databricks and AWS.
• Build, monitor, and optimize automated pipelines for training, testing, and deployment.
• Establish standards for model versioning, testing, release controls, and monitoring.
• Design and scale model-serving infrastructure for latency, reliability, and cost.
• Lead investigations and resolve complex production issues across the ML lifecycle.
• Document repeatable workflows, review designs, and mentor colleagues in MLOps best practices.
• Create dashboards to track model performance, reliability, and infra costs.

Skills

Databricks

AWS workflows

Python

SQL

PySpark

MLflow

Delta Live Tables

CI/CD

Monitoring systems

Productions issues

Tools

Databricks

AWS

Python

MLflow

Delta Live Tables

CI/CD pipelines

Job description

Senior MLOps Engineer – ML Leadership Role at Fluent — Remote

This is a senior individual contributor with technical leadership responsibilities — a role that sits between pure engineering and engineering management. It's ideal for MLOps professionals who want to shape standards and mentor peers without moving into full people management.

Compensation benchmark: The CAD $130,000–$140,000 range (plus bonus) is at or slightly below market median for Senior MLOps Engineers in major Canadian tech hubs (Toronto/Vancouver typically see CAD $140,000–$170,000+). The bonus component could close the gap. If you're in a lower cost-of-living Canadian city, this is competitive.

• Databricks and MLflow at production scale — both highly marketable in 2024–2025 ML hiring
• Low-latency real-time serving architecture on AWS — transferable to any ML-heavy tech company
• CI/CD for ML pipelines — foundational for senior ML engineering roles
• Technical mentorship and engineering standards ownership — critical for future Staff/Principal Engineer or ML Engineering Manager trajectories

Green flags: NASDAQ-listed company (financial stability), clear scope and ownership language, cross-functional collaboration with ML scientists and Product, explicit mentorship expectations signal a learning-oriented culture.

Watch for: The benefits section emphasizes in-office perks (catered meals, office events) which may suggest a predominantly office-based culture despite the Canada-wide hiring scope. Clarify remote vs. hybrid expectations before accepting. The E-Verify mention is unusual for a Canadian role and may reflect US parent company processes.

We are seeking a Senior MLOps Engineer based in Canada to join our data and machine learning team. This role will co-own MLOps practices at Fluent Media and establish standards for model deployment, serving, scaling, and infrastructure efficiency. Working with ML scientists, Data Engineering, and Product, this engineer will ensure models support accurate ad selection through reliable production systems at an optimal cost to serve.

What You'll Do

• Partner with ML scientists to operationalize models in Databricks and AWS.
• Build, monitor, and optimize automated pipelines for training, testing, and deployment.
• Establish standards for model versioning, automated testing, release controls, and performance monitoring.
• Design and scale model-serving infrastructure to meet latency, reliability, and cost requirements.
• Lead investigations and resolve complex production issues across the ML lifecycle.
• Document repeatable workflows, review designs and code, and mentor colleagues in MLOps best practices.
• Create and maintain dashboards to track model performance, reliability, and infrastructure cost.

Requirements

• Strong experience with Databricks (preferred) and AWS-based ML workflows.
• Proficiency with Python, SQL, PySpark, and related ML and data engineering tools.
• Experience with ML lifecycle management tools such as MLflow and Delta Live Tables.
• Proven ownership of architecture and operations for real-time, low-latency relevance models and serving endpoints.
• Hands-on experience with CI/CD, automated testing, and monitoring systems.
• Ability to independently resolve production challenges and collaborate with ML scientists, engineers, and Product.
• Experience reviewing pull requests, establishing engineering standards, and mentoring colleagues.

Nice-to-Haves

• Knowledge of data governance, security, and compliance in cloud environments.
• Familiarity with model observability and explainability frameworks.
• Experience with stream processing or near-real-time pipelines.
• Knowledge of digital marketing or mar-tech metrics.

Profile Fit

This is a hands-on technical leadership role, best suited for someone who can independently design reliable ML systems, establish engineering standards, and strengthen the team's production capabilities. The role requires sound judgment across model performance, reliability, scalability, and infrastructure cost.

About Us

Fluent, Inc. (NASDAQ: FLNT) is a commerce media solutions provider connecting top-tier brands with highly engaged consumers. Leveraging diverse ad inventory, robust first-party data, and proprietary machine learning, Fluent unlocks additional revenue streams for partners and empowers advertisers to acquire their most valuable customers at scale. Founded in 2010, Fluent uses its deep expertise in performance marketing to drive monetization and increase engagement at key touchpoints across the customer journey. For more insights visit: https://www.fluentco.com/

At Fluent, we like what we do, and we like who we do it with. Our team is a tight-knit crew of go-getters; we love to celebrate our successes! In addition, we offer a fully stocked kitchen, catered breakfast and lunch, and our office manager keeps the calendar stocked with activity-filled events. When we are not eating, working out, or planning parties, Fluent folks can be found participating in recreational sports leagues, networking with She Runs It, and bonding across teams during quarterly outings to baseball games, fancy dinners, and pizza-making classes. And we have all the practical benefits, too…

• Competitive compensation
• Ample career and professional growth opportunities
• Health, dental, and vision insurance
• Pre-tax savings plans and transit/parking programs
• 401K with competitive employer match
• Volunteer and philanthropic activities throughout the year
• Educational and social events
• The amazing opportunity to work for a high-flying performance marketing company!

Salary Range: $130,000 to $140,000 CAD plus competitive bonus. The base salary range represents the low and high end of the Fluent salary range for this position. Actual salaries will vary depending on factors including but not limited to location, experience, and performance.

Candidates may be at risk of targeting by malicious actors seeking personal information. Fluent recruiters will only reach out via LinkedIn or email with an @fluentco.com domain. Any outreach by Fluent via other sources (e.g. text, other domains, etc.) should be ignored.

Fluent participates in the E-Verify Program. As a participating employer, Fluent, LLC will provide the Social Security Administration (SSA) and, if necessary, the Department of Homeland Security (DHS), with information from each new employee's Form I-9 to confirm work authorization. Fluent, LLC follows all federal regulations including those set forth by The Office of Special Counsel for Immigration-Related Unfair Employment Practices (OSC). The OSC enforces the anti-discrimination provision (274B) of the Immigration and Nationality Act (INA), 8 U.S.C. 1324b.

Is this MLOps Engineer role fully remote or does it require office attendance? The posting requires candidates to be based in Canada but does not specify a mandatory office location. However, the benefits section references in-office perks, so candidates should clarify the remote vs. hybrid arrangement directly with Fluent during the interview process. What is the salary for this Senior MLOps Engineer position at Fluent? The base salary range is CAD $130,000 to $140,000 per year, plus a competitive bonus. Actual compensation varies based on location, experience, and performance. What are the core technical skills required for this role? The role requires strong experience with Databricks, AWS-based ML workflows, Python, SQL, PySpark, MLflow, Delta Live Tables, and CI/CD pipelines. Experience with real-time, low-latency model serving is essential. Does Fluent offer visa sponsorship for this Canadian role? The posting does not mention visa sponsorship. Candidates must be eligible to work in Canada. Interested international applicants should contact Fluent's recruiting team directly to clarify work authorization requirements. What kind of leadership is expected in this Senior MLOps Engineer role? This is a hands-on technical leadership role. You will establish MLOps engineering standards, review pull requests, mentor colleagues, and co-own MLOps practices — but it is not explicitly a people management position.

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