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Python Ml Developer Jobs in Mount Laurel, NJ (NOW HIRING)

Accenture

Google Data Specialist

Philadelphia, PA · On-site

$70K - $196K/yr

Collaborate closely with senior data engineers, ML engineers, and architects. * Contribute to ... Minimum of 2 years of experience with Python or AI or GenAI tools (Vertex AI preferred). * Bachelor ...

Munich Re

Data Scientist

Philadelphia, PA · On-site +1

Act as a liaison between the ML Engineers and Data Science team, managing AI/ML model deployments ... Proficiency in statistical programming languages such as R, Python, SAS, or similar, alongside ...

Munich RE

Data Scientist

Philadelphia, PA · On-site

Act as a liaison between the ML Engineers and Data Science team, managing AI/ML model deployments ... Proficiency in statistical programming languages such as R, Python, SAS, or similar, alongside ...

Bespoke Labs

Machine Learning Engineer

Trenton, NJ · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Damco Solutions

GenAI Architect

Mount Laurel, NJ · On-site

... programming skills in Python and experience with ML frameworks. • Hands‐on experience with LLMs; transformers; and generative AI architectures. • Experience with prompt engineering ...

Tech Tammina LLC

Azure Data Engineer

Hamilton, NJ · On-site

$113K - $136K/yr

Build and optimize ETL/ELT workflows using PySpark, Python, SQL, and Azure Data Factory/Fabric ... Support analytics and AI/ML use cases with clean, reliable, and scalable data models. * Collaborate ...

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All JobsPython Ml Developer JobsPython Ml Developer Jobs in Mount Laurel, NJ

Python Ml Developer information

See Mount Laurel, NJ salary details

$13

$58

$85

How much do python ml developer jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for python ml developer in Mount Laurel, NJ is $58.05, according to ZipRecruiter salary data. Most workers in this role earn between $47.84 and $65.96 per hour, depending on experience, location, and employer.

What does a Python ML Developer do?

A Python ML Developer designs, builds, and deploys machine learning models using the Python programming language. They work with large datasets, clean and process data, select appropriate algorithms, and use libraries like TensorFlow, PyTorch, or scikit-learn to implement solutions. Their work often involves collaborating with data scientists and engineers to integrate machine learning models into applications. Additionally, they may be responsible for testing, tuning, and optimizing models to achieve the best possible performance in real-world scenarios.

What are the key skills and qualifications needed to thrive as a Python ML Developer?

To thrive as a Python ML Developer, you need strong programming skills in Python, a solid understanding of machine learning algorithms, and a background in mathematics or statistics, often supported by a degree in computer science, engineering, or a related field. Familiarity with tools and libraries such as TensorFlow, scikit-learn, PyTorch, and version control systems like Git is essential, along with experience using data visualization and cloud platforms. Critical soft skills include problem-solving, adaptability, and effective communication to collaborate with cross-functional teams and explain complex models to stakeholders. These skills ensure the successful development, deployment, and maintenance of machine learning solutions that drive business value.

What are some common challenges Python ML Developers face when deploying machine learning models to production?

Python ML Developers often encounter challenges such as ensuring model scalability, managing dependencies, and maintaining reproducibility when deploying models into production environments. Integrating machine learning models with existing systems can require close collaboration with DevOps and software engineering teams to streamline workflows and automate deployment pipelines. Additionally, monitoring model performance over time and handling data drift are crucial responsibilities to ensure continued accuracy and reliability of deployed solutions.

What is the difference between Python Ml Developer vs Data Scientist?

AspectPython Ml DeveloperData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; Python, ML certificationsBachelor's/Master's in Data Science, Statistics, or related; Python, ML certifications
Work EnvironmentSoftware development teams, AI/ML projectsResearch, data analysis, modeling teams
Employer & Industry UsageTech companies, startups, AI firmsFinance, healthcare, tech, research institutions
Common Search & ComparisonYesYes

Python ML Developers focus on building and deploying machine learning models using Python, often working closely with software engineering teams. Data Scientists analyze data, create models, and generate insights, often using Python along with statistical tools. While both roles require Python and ML knowledge, Python ML Developers are more involved in implementation and deployment, whereas Data Scientists focus on data analysis and research.

What job categories do people searching Python Ml Developer jobs in Mount Laurel, NJ look for?

The top searched job categories for Python Ml Developer jobs in Mount Laurel, NJ are:

What cities near Mount Laurel, NJ are hiring for Python Ml Developer jobs?

Cities near Mount Laurel, NJ with the most Python Ml Developer job openings:

Python Ml Developer jobs near you
Infographic showing various Python Ml Developer job openings in Mount Laurel, NJ as of August 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $120,752 per year, or $58.1 per hour.

Principal Machine Learning Engineer

Delan Associates, Inc.

Philadelphia, PA • On-site

Contractor

Re-posted 5 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

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