

HCM Staffing and Consulting Group
Machine Learning/AI Contractor
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning/AI Contractor with a contract length of "unknown," offering a pay rate of "unknown." Candidates must have advanced Node.JS skills (6-9 years), expertise in Python AI, and experience with MLOps, Kafka, and ML frameworks like TensorFlow and PyTorch.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
464
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🗓️ - Date
January 30, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#Automation #ML (Machine Learning) #TensorFlow #PyTorch #ML Ops (Machine Learning Operations) #Kafka (Apache Kafka) #AI (Artificial Intelligence) #Python #Model Evaluation #NLP (Natural Language Processing) #Monitoring #Data Engineering
Role description
Job Description:
You will deliver machine learning and artificial intelligence solutions to support the analytical and automation needs of the project team. Develop, train, and implement machine learning and AI models using relevant data sources. Apply techniques such as natural language processing, computer vision, or predictive analytics as needed for project objectives. Conduct data preprocessing, feature selection, and model evaluation to ensure solution quality. Deploy AI-driven applications and monitor their ongoing performance in production environments. Document methodologies and collaborate with technical team members to align solutions with project requirements.
Primary Skill Required for the Role: Node.JS
Level Required for Primary Skill: Advanced (6-9 years experience)
Additional Skills Requested for Role:
• Python AI
• Machine Learning
• Kafka
• Pytorch
• Machine Learning Operations (MLOps)
Additional Details for Role:
• Need Applied ML Engineer to design, deploy, and operationalize machine-learning models that power the Decision Engine, enabling intelligent selection of the most relevant decision using real-time and historical data.
• This role focuses on building and integrating Python-based ML models (e.g., propensity, ranking, uplift, or optimization models), serving models via low-latency APIs, and integrating with feature stores, streaming pipelines (Kafka or equivalent), and decisioning services built in Node.js and/or Python.
• The ideal candidate has strong experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn), MLOps practices (model versioning, monitoring, retraining), and understands explainability, bias, and guardrails in regulated environments.
• The engineer will collaborate closely with data engineers, rules engineers, and backend teams to ensure models are reliable, observable, and safely embedded into production decision workflows at scale.
Job Description:
You will deliver machine learning and artificial intelligence solutions to support the analytical and automation needs of the project team. Develop, train, and implement machine learning and AI models using relevant data sources. Apply techniques such as natural language processing, computer vision, or predictive analytics as needed for project objectives. Conduct data preprocessing, feature selection, and model evaluation to ensure solution quality. Deploy AI-driven applications and monitor their ongoing performance in production environments. Document methodologies and collaborate with technical team members to align solutions with project requirements.
Primary Skill Required for the Role: Node.JS
Level Required for Primary Skill: Advanced (6-9 years experience)
Additional Skills Requested for Role:
• Python AI
• Machine Learning
• Kafka
• Pytorch
• Machine Learning Operations (MLOps)
Additional Details for Role:
• Need Applied ML Engineer to design, deploy, and operationalize machine-learning models that power the Decision Engine, enabling intelligent selection of the most relevant decision using real-time and historical data.
• This role focuses on building and integrating Python-based ML models (e.g., propensity, ranking, uplift, or optimization models), serving models via low-latency APIs, and integrating with feature stores, streaming pipelines (Kafka or equivalent), and decisioning services built in Node.js and/or Python.
• The ideal candidate has strong experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn), MLOps practices (model versioning, monitoring, retraining), and understands explainability, bias, and guardrails in regulated environments.
• The engineer will collaborate closely with data engineers, rules engineers, and backend teams to ensure models are reliable, observable, and safely embedded into production decision workflows at scale.






