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Python Ml Developer Jobs in Colorado (NOW HIRING)

ML Engineer Location: Colorado Springs, CO or DC or Remote Security Clearance: Uncleared (Secret ... Deep expertise in Python and modern ML frameworks (PyTorch, TensorFlow). * RF / Signal Processing:

Principal AI/ML Engineer Location: Englewood, CO Zip Code: 80112 Duration: 6 Months Pay Rate: $ 90 ... Strong proficiency in Python, C++, C#, and/or Java with experience building scalable Machine ...

New

AI/ML Engineer II

Lone Tree, CO ยท On-site

$99K - $136K/yr

Proficiency in programming languages such as Python, C++, C# or Java. * Strong understanding of supervised and unsupervised learning techniques. * Experience deploying AI/ML solutions in production ...

Principal AI/ML Engineer Location: Englewood, CO Zip Code: 80112 Duration: 6 Months Pay Rate: $ 90 ... Strong proficiency in Python, C++, C#, and/or Java with experience building scalable Machine ...

New

AI/ML Engineer II

Highlands Ranch, CO ยท On-site

$96K - $131K/yr

Proficiency in programming languages such as Python, C++, C# or Java. * Strong understanding of supervised and unsupervised learning techniques. * Experience deploying AI/ML solutions in production ...

AI/ML Engineer II

Lone Tree, CO

$99K - $136K/yr

Proficiency in programming languages such as Python, C++, C# or Java. * Strong understanding of supervised and unsupervised learning techniques. * Experience deploying AI/ML solutions in production ...

Sr AI/ML Engineer

Lone Tree, CO

$106K - $146K/yr

Strong proficiency in programming languages such as Python, C++, C# or Java, with experience in building scalable AI/ML systems. * Demonstrated experience leading teams or projects, including ...

Sr AI/ML Engineer

Longmont, CO

$103K - $141K/yr

Strong proficiency in programming languages such as Python, C++, C# or Java, with experience in building scalable AI/ML systems. * Demonstrated experience leading teams or projects, including ...

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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 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.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and maintain AI and machine learning systems. While AI automation tools can handle certain tasks, MLEs are essential for creating, optimizing, and interpreting complex models, making complete replacement unlikely in the near term. MLEs need skills in programming, data analysis, and model deployment to adapt to evolving AI technologies.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-paying position in artificial intelligence, such as senior machine learning engineer or AI research director, often requiring advanced skills in deep learning, data science, and programming with tools like Python and TensorFlow. Such roles usually involve leadership, strategic planning, and extensive experience in the field.

Which 3 jobs will survive AI?

For a Python ML Developer, roles that require complex problem-solving, creativity, and human judgment are likely to persist, such as AI research scientist, data scientist, and software engineer. These jobs involve designing, interpreting, and improving AI models, which currently require advanced expertise, critical thinking, and domain knowledge that AI cannot fully replicate. Continuous learning and staying updated with new tools and techniques are essential for long-term career resilience.

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

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 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.

Can you do ML in Python?

Yes, Python is widely used for machine learning (ML) development due to its extensive libraries such as TensorFlow, scikit-learn, and PyTorch. Python skills are essential for a Python ML developer to build, train, and deploy ML models efficiently in various environments.
What job categories do people searching Python Ml Developer jobs in Colorado look for? The top searched job categories for Python Ml Developer jobs in Colorado are:
What cities in Colorado are hiring for Python Ml Developer jobs? Cities in Colorado with the most Python Ml Developer job openings:
Infographic showing various Python Ml Developer job openings in Colorado as of July 2026, with employment types broken down into 83% Full Time, 7% Part Time, 1% Temporary, and 9% Contract. Highlights an 80% Physical, 4% Hybrid, and 16% Remote job distribution.

ML Engineer

Omni Federal

Colorado Springs, CO โ€ข On-site

Full-time

Posted 14 days ago


Job description

Job Description

Job Title: ML Engineer

Location: Colorado Springs, CO or DC or Remote

Security Clearance: Uncleared (Secret Preferred) or must be willing to obtain

We question. We listen. We adapt.

Be honest. Be pragmatic.

Omni Federal, founded in 2017 and headquartered in Washington, DC, is a highly specialized software solutions provider with a robust presence in key locations across the United States, including Boston, MA, Colorado Springs, CO, San Antonio, TX, and St. Louis, MO. Born out of the Department of Defense's software factory ecosystem, Omni has rapidly distinguished itself by delivering both mission-critical and enterprise solutions that enhance the technological capabilities of the federal government. With a focus on areas such as Command and Control, Cybersecurity, Space, Geospatial, and Modeling Simulation, Omni leverages cutting-edge commercial technology tailored to government objectives, improving mission performance and delivering transformative outcomes for the Department of Defense (DoD), Intelligence Community (IC), and their end-users. The company's innovative approach is backed by its Omni Labs and SBIR Innovation centers, where they develop advanced platforms and tools in data mesh, secure connectivity, and intelligent automation

Why Omni?

  • Environment of Autonomy

  • Innovative Commercial Approach

  • People over process

We are seeking a strong Machine Learning Engineer to support the software and ML implementation of a groundbreaking Space Domain Awareness (SDA) initiative.Partnering closely with the Virginia Tech National Security Institute (VTNSI) and building upon successful organic research, you will help field a GEO-capable passive RF "voiceprint" combat ID capability. The research team has already demonstrated a functional, on-sky Unique Emitter ID (UEID) method using raw I/Q data to perform combat ID on Resident Space Objects (RSOs). Your mission is to bring this technology out of the lab and into active operations. You will be the sole Omni individual driving the implementation of this demonstrator, working alongside VTNSI researchers to deliver a fully autonomous, taskable capability to the Joint Commercial Office. Candidates must be passionate, energized, and excited to work on modern architectures and solve challenging problems for our clients

What You Will Do:

  • Algorithm Implementation: Transition existing organic UEID research into a robust, production-ready machine learning pipeline capable of analyzing raw passive RF I/Q datato extract RSO "voiceprints."

  • Data Integration: Engineer the connections required to ingest live sensor data from theVTNSI data source and route it effectively through the processing pipeline.

  • UDL Tech Transfer: Develop and standardize the output for the new Unified Data Library (UDL) Passive RF I/Q endpoint, ensuring the techniques developed can be seamlessly transferred to any other willing passive RF provider.

  • Autonomous Tasking: Integrate the completed passive RF combat ID capability into the IL2 MACHINA environment, enabling autonomous tasking and scheduling.

  • JCO Delivery: Ensure the final demonstrator and risk-reduction sensor meet the strict operational requirements for delivery to the Joint Commercial Operations (JCO) cell.


The Hard Part (Why Youโ€™ll Love This Role)

Finding talent for this specific intersection of skills is notoriously difficult. This isn't a standard computer vision or NLP machine learning role; it requires a deep understanding of RF signal processing and the ability to train models on raw, complex I/Q data rather than clean, pre-processed datasets. You will be operating at the bleeding edge of GEO passive RF tracking. If you are the kind of engineer who thrives on solving niche, highly technical problems that most ML practitioners have never even encountered, you will have the autonomy and runway to make a massive impact here.

Required Qualifications:

  • Experience: 4+ years of experience as a Machine Learning Engineer, Data Scientist, or closely related role.

  • Technical Skills: Deep expertise in Python and modern ML frameworks (PyTorch, TensorFlow).

  • RF / Signal Processing: Demonstrated experience working with Radio Frequency (RF) systems, specifically handling, parsing, and applying machine learning techniques to raw I/Q data.

  • Software Engineering: Strong capability in building production-ready pipelines, API integrations, and deploying models into operational environments.

Preferred Qualifications:

  • Active DoD Security Clearance.

  • Previous experience collaborating with academic or research institutions (e.g., VTNSI).

  • Familiarity with Space Domain Awareness (SDA), satellite communications, or GEO RSO tracking.

  • Experience working with the Unified Data Library (UDL), MACHINA, or delivering capabilities to the JCO.

  • Background in automated scheduling or autonomous sensor tasking.



Our team is always hungry for more โ€“ more knowledge, more problem solving, more growth, more innovation, and, ultimately, customer success. This hunger and hustle fuel our determination to excel in everything we do. Itโ€™s not just about meeting established goals; itโ€™s about exceeding them. We take immense pride in applying our expertise in cutting-edge technologies and our ability to adapt to emerging trends directly to the users in the field, as our nationโ€™s protectors deserve nothing less.


Omni Federal is an equal opportunity and affirmative action employer. Omni Federal is committed to administering all employment and personnel actions on the basis of merit and free of discrimination based on race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or status as an individual with a disability. Consistent with this commitment, we are dedicated to the employment and advancement of qualified minorities, women, individuals with disabilities, protected veterans, persons of all ethnic backgrounds and religions according to their abilities.


Proposed Pay Scale: $180,000 - $250,000

, About Omni Federal

Omni Federal, founded in 2017 and headquartered in Washington, DC, is a highly specialized software solutions provider with a robust presence in key locations across the United States, including Boston, MA, Colorado Springs, CO, San Antonio, TX, and St. Louis, MO. Born out of the Department of Defense's software factory ecosystem, Omni has rapidly distinguished itself by delivering both mission-critical and enterprise solutions that enhance the technological capabilities of the federal government. With a focus on areas such as Command and Control, Cybersecurity, Space, Geospatial, and Modeling Simulation, Omni leverages cutting-edge commercial technology tailored to government objectives, improving mission performance and delivering transformative outcomes for the Department of Defense (DoD), Intelligence Community (IC), and their end-users. The company's innovative approach is backed by its Omni Labs and SBIR Innovation centers, where they develop advanced platforms and tools in data mesh, secure connectivity, and intelligent automation.