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Python Ml Developer Jobs in Illinois (NOW HIRING)

Co-Sourcing Partners

GCP AI/ML Engineer

Chicago, IL · On-site

Job Title: GCP AI/ML Engineer Duration: 6 months Contract to hire Location: Chicago is the ... Orchestrate ML workflows using Python, Vertex AI, BigQuery, and Cloud Storage. * Ensure pipelines ...

Resource Point LLC

AI/ML Engineer

Chicago, IL · On-site

Position: AI/ML Engineer Duration: 12 months Location: Chicago, IL An AI/ML Engineer designs ... Proficiency in Python, Java, or R, Golang * Machine Learning Frameworks : Expertise in tools and ...

Inspire11

ML Engineering Consultant

Chicago, IL · On-site

... and DevOps • Expertise in data preprocessing and ML techniques • Experience with model ... skills (Python, R) • Cloud services and infrastructure knowledge • Agile methodologies ...

Catalyst Labs

ML Engineer

Chicago, IL · On-site

... Python (TypeScript experience is a plus). • Hands-on experience with ML frameworks such as ... feature engineering, evaluation strategies, and deployment best practices. Preferred : • ...

Equiforte

Senior ML Engineer

Chicago, IL · On-site

$125 - $150/hr

As a Senior ML Engineer at Equiforte, you will develop, train, and deploy the machine learning ... Strong proficiency in Python and modern ML frameworks (PyTorch or JAX preferred) * Experience ...

NAVA Software Solutions

Sr. AI/ML Engineer

Deerfield, IL · On-site

$106K - $145K/yr

Strong Python engineering background with ML/DL frameworks: TensorFlow, PyTorch, Keras, OpenCV * Proven experience in Computer Vision tasks, including object detection, segmentation, and OCR

YO AI Labs

AI/ML Engineer - Remote

Chicago, IL · Remote

$200 - $350/hr

Follow modern secure coding, DevOps, and CI/CD practices. * Document technical decisions and ... Strong Python proficiency for AI/ML development, including REST APIs and SDK integrations. * Hands ...

Strategic Employment

Python Engineer

Chicago, IL · Hybrid

Python Engineer An established healthcare company is looking for a driven Python Engineer to join ... AWS SageMaker experience will be a big plus as they're in the process of bringing ML development in ...

Octave X

ML Engineer

Chicago, IL · On-site

$200 - $250/hr

The role bridges applied ML engineering and production reliability for regulated and high-trust ... Strong Python skills with deep experience in PyTorch and/or JAX. * Hands-on experience with ...

Northern Base

Senior AI ML Engineer

Chicago, IL · On-site

$120K - $130K/yr

ML engineering/Applied AI; production ML deployment; RAG and Agentic AI systems ... Python; model serving frameworks and API development such as FastAPI/MLflow; vector databases and ...

Steampunk

MLOps Engineer

Bloomington, IL · On-site

$125 - $150/hr

... DevOps, cloud engineering, or applied ML development. * Proficiencyin Python and familiarity with ML frameworks such as scikit-learn, TensorFlow,PyTorch, orXGBoost. * Hands-on experience with at ...

Tror AI for everyone

AI Developer

Mettawa, IL · On-site

$80/hr

Senior AI Developer Location: Mettawa, IL (Onsite) Rate: $80/hr on C2C Design, build, and deploy ... Expert-level proficiency in Python and related AI/ML frameworks (e.g., PyTorch, TensorFlow ...

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Python Ml Developer information

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 are popular job titles related to Python Ml Developer jobs in Illinois?

For Python Ml Developer jobs in Illinois, the most frequently searched job titles are:

What job categories do people searching Python Ml Developer jobs in Illinois look for?

The top searched job categories for Python Ml Developer jobs in Illinois are:

What cities in Illinois are hiring for Python Ml Developer jobs?

Cities in Illinois with the most Python Ml Developer job openings:

Python Ml Developer jobs near you
Infographic showing various Python Ml Developer job openings in Illinois as of August 2026, with employment types broken down into 1% Internship, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 76% Physical, 6% Hybrid, and 18% Remote job distribution.

GCP AI/ML Engineer

Chicago, IL • On-site

Co-Sourcing Partners
51 - 200 employees
Learn more about Co-Sourcing Partnersexternal

Contractor

Re-posted 7 days ago

Job description

Job Title: GCP AI/ML Engineer
Duration: 6 months Contract to hire
Location: Chicago is the preferred location, but open to candidates from anywhere in the U.S.
Role Overview
We are seeking a talented and experienced GCP AI/ML Engineer to design, build, and operationalize scalable machine learning solutions on Google Cloud Platform (GCP). This role focuses on developing production-grade ML pipelines, automating workflows, and ensuring reliability and governance across enterprise AI platforms.
The ideal candidate will have strong expertise in Vertex AI, MLOps, and cloud-native ML architectures, with a passion for turning data science models into scalable, production-ready systems.
Key Responsibilities
ML Pipeline Development & Automation
  • Build, deploy, and manage production-grade machine learning pipelines using Vertex AI Pipelines and GCP-native services.
  • Design automated workflows for data ingestion, feature engineering, model training, evaluation, and inference.
  • Orchestrate ML workflows using Python, Vertex AI, BigQuery, and Cloud Storage.
  • Ensure pipelines are modular, reusable, and scalable across use cases.

Model Operationalization (MLOps)
  • Operationalize the end-to-end ML lifecycle, including:
  • Model training
  • Deployment
  • Monitoring
  • Retraining and lifecycle management
  • Deploy models using Vertex AI endpoints with support for online and batch predictions.
  • Implement robust CI/CD pipelines for ML artifacts and workflows.
  • Enable automated model retraining and versioning strategies.

Data Integration & Feature Engineering
  • Enable seamless data flows across data lakes, warehouses, and ML platforms.
  • Design and manage feature pipelines for training and inference datasets.
  • Integrate with BigQuery, Cloud Storage, and streaming sources to support real-time and batch ML use cases.
  • Ensure consistency between training and serving data pipelines.

Model Monitoring & Performance Optimization
  • Implement model monitoring solutions to track:
  • Prediction accuracy
  • Data drift and concept drift
  • Model performance degradation
  • Set up alerting mechanisms and dashboards for proactive issue detection.
  • Optimize model performance and infrastructure for scalability, latency, and cost efficiency.

AI Platform Engineering
  • Build and enhance enterprise AI/ML platforms with a focus on:
  • Automation
  • Observability
  • Reliability
  • Develop standardized frameworks for repeatable and governed ML deployments.
  • Establish best practices for MLOps, pipeline orchestration, and infrastructure management.

Collaboration & Cross-Functional Engagement
  • Collaborate closely with:
  • Data Scientists to productionize models
  • Data Engineers for data pipeline integration
  • Architects for scalable cloud designs
  • Translate business requirements into deployable ML solutions.
  • Provide technical leadership and mentoring on ML engineering practices.

Governance, Security & Best Practices
  • Implement model governance frameworks including auditability, lineage, and compliance.
  • Ensure secure handling of data and models using IAM roles and access policies.
  • Promote best practices in:
    • Code versioning (Git)
    • CI/CD
    • Testing and validation
  • Drive documentation and standardization across ML workflows.

Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field.
  • 4+ years of experience in machine learning engineering or MLOps.
  • Hands-on experience with Google Cloud Platform (GCP) services:
  • Vertex AI (Pipelines, Training, Endpoints)
    oBigQuery
    oCloud Storage
  • Strong programming skills in Python.
  • Experience building and deploying end-to-end ML pipelines.
  • Strong understanding of ML lifecycle and MLOps principles.

Preferred Skills
  • Experience with TensorFlow, PyTorch, or Scikit-learn.
  • Familiarity with Kubeflow Pipelines or Apache Beam.
  • Experience with Docker and containerized deployments.
  • Knowledge of real-time ML inference and streaming architectures.
  • Hands-on experience with model monitoring tools and frameworks.
  • Understanding of feature stores and feature engineering pipelines.

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