ZipRecruiter

Log In

1

Senior Machine Learning Ops Engineer Jobs in Philadelphia, PA

Slalom Consulting

General Information

Philadelphia, PA · On-site

$60.50 - $78.75/hr

Description and Requirements AI/ML Ops Engineer Location: Remote / Hybrid (Client-Facing Consulting ... Engineer to design, deploy, and operate production-grade machine learning and Generative AI ...

previous pagenext page

Showing results 21-40

All JobsMachine Learning Ops Engineer JobsSenior Machine Learning Ops Engineer JobsSenior Machine Learning Ops Engineer Jobs in Philadelphia, PA

Senior Machine Learning Ops Engineer information

See Philadelphia, PA salary details

$60K

$127.7K

$185.2K

How much do senior machine learning ops engineer jobs pay per year?

As of Sep 12, 2026, the average yearly pay for senior machine learning ops engineer in Philadelphia, PA is $127,707.00, according to ZipRecruiter salary data. Most workers in this role earn between $105,400.00 and $144,800.00 per year, depending on experience, location, and employer.

What is a senior machine learning ops engineer?

Senior Machine Learning Ops (MLOps) Engineers are experienced professionals who design, build, and maintain the infrastructure and tools needed to deploy, monitor, and scale machine learning models in production environments. They work at the intersection of data science, software engineering, and DevOps to ensure ML models are robust, reliable, and secure. Their responsibilities often include automating model training pipelines, managing cloud resources, implementing CI/CD for ML, and ensuring model reproducibility. Senior MLOps Engineers also mentor junior staff and help define best practices for the organization’s ML workflow.

What are the key skills and qualifications needed to thrive as a senior machine learning ops engineer?

To thrive as a Senior Machine Learning Ops Engineer, you need expertise in machine learning, software engineering, cloud platforms, and experience with CI/CD pipelines, often supported by a computer science degree or equivalent experience. Proficiency with tools like Docker, Kubernetes, TensorFlow, PyTorch, and cloud services such as AWS, GCP, or Azure is typically required, along with familiarity with MLOps frameworks. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and manage complex ML model deployments. These skills are essential to ensure reliable, scalable, and efficient deployment of machine learning models in production environments.

What are some common challenges faced by senior machine learning ops engineers when deploying models to production?

Senior Machine Learning Ops Engineers often encounter challenges such as ensuring model reproducibility, managing model versioning, and automating deployment pipelines for scalability. Another key challenge is monitoring model performance and data drift in production, which requires robust logging and alerting systems. Collaborating closely with data scientists, software engineers, and IT teams is essential to address these challenges and maintain a stable, efficient ML infrastructure.

What is the difference between Senior Machine Learning Ops Engineer vs Data Engineer?

AspectSenior Machine Learning Ops EngineerData Engineer
CredentialsExperience with ML frameworks, cloud platforms, scripting, and DevOps toolsStrong SQL, ETL, database, and programming skills, often with cloud experience
Work EnvironmentFocus on deploying, monitoring, and maintaining ML models in productionDesigning and building data pipelines and infrastructure for data processing
Industry UsageCommon in AI/ML-focused companies, tech firms, and data-driven organizationsWidespread across industries for data management and analytics

While both roles involve working with data and cloud platforms, the Senior Machine Learning Ops Engineer specializes in deploying and maintaining machine learning models, whereas the Data Engineer focuses on building data pipelines and infrastructure. Understanding these distinctions helps in choosing the right career path or job search focus.

What are popular job titles related to Senior Machine Learning Ops Engineer jobs in Philadelphia, PA?

For Senior Machine Learning Ops Engineer jobs in Philadelphia, PA, the most frequently searched job titles are:

What job categories do people searching Senior Machine Learning Ops Engineer jobs in Philadelphia, PA look for?

The top searched job categories for Senior Machine Learning Ops Engineer jobs in Philadelphia, PA are:

Senior Machine Learning Ops Engineer jobs near you
Infographic showing various Senior Machine Learning Ops Engineer job openings in Philadelphia, PA as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 22% Part Time, and 1% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $127,707 per year, or $61.4 per hour.

Principal Machine Learning Engineer

Philadelphia, PA • On-site

Delan Associates, Inc
Engineering Professional Services • 51 - 200 employees
Learn more about Delan Associates, Incexternal

Contractor

Re-posted 28 days ago

Job description

Principal Machine Learning Engineer to serve as a hands-on technical leader for machine learning, predictive modeling, scoring, decisioning, and applied AI initiatives. This role will primarily focus on building, validating, deploying, and improving machine learning models, while also bringing principal-level judgment to problem definition, model design, stakeholder engagement, and production readiness.

Hands-On Model Development

Build, test, validate, and improve machine learning models for scoring, prediction, prioritization, risk detection, engagement, intervention targeting, and decision support.

Perform exploratory data analysis, data quality assessment, feature engineering, model training, model selection, and performance evaluation.

Develop practical ML models that balance predictive performance, explainability, stability, maintainability, and business usefulness.

Work with structured, semi-structured, and operational data to create model-ready datasets and reusable features.

Use tools such as Python, SQL, Spark, Databricks, MLflow, scikit-learn, XGBoost, or similar platforms and libraries.

Move quickly from data exploration to prototype to validated model to production-ready capability.

Required Qualifications

Professional experience in machine learning, data science, software engineering, analytics engineering, applied AI, or related technical fields.

5+ years of hands-on machine learning model development experience, including feature engineering, model training, validation, evaluation, and iteration.

3+ years of experience deploying, operationalizing, or supporting models in production or business-critical environments.

Strong hands-on experience with Python and SQL.

Experience with modern ML and data platforms such as Databricks, Spark, MLflow, Snowflake, Azure, AWS, or similar technologies.

Strong understanding of model evaluation, calibration, thresholding, score interpretation, monitoring, drift, retraining, and production ML lifecycle management.

Experience translating ambiguous business problems into concrete ML designs, model requirements, validation plans, and measurable outcomes.

Ability to explain model behavior, model performance, assumptions, limitations, and tradeoffs to both technical and non-technical stakeholders.

Strong engineering discipline, including clean code, reproducibility, versioning, testing, documentation, and maintainability.

Ability to work independently as a senior hands-on contributor while also providing technical leadership and modeling judgment.

Scoring, Scorecards, and Transparent Models

Production ML and MLOps

Product and Rapid-Build Execution

Generative AI and AI Automation

Requirement Shaping and Stakeholder Partnership

Employment Type: CONTRACTOR

Apply