

AI/ML Engineer
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Machine Learning Engineer (Generative AI Focus) with a long-term contract in Newark, NJ, offering a pay rate of "X". Requires 5+ years of ML experience, proficiency in Python, and expertise in GenAI frameworks and cloud platforms.
π - Country
United States
π± - Currency
$ USD
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π° - Day rate
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ποΈ - Date discovered
September 11, 2025
π - Project duration
Unknown
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ποΈ - Location type
Hybrid
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π - Contract type
Unknown
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π - Security clearance
Unknown
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π - Location detailed
Newark, NJ
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π§ - Skills detailed
#Python #GIT #Kubernetes #Cloud #Monitoring #Data Pipeline #Storage #Version Control #Computer Science #Deployment #Data Engineering #ML (Machine Learning) #Hugging Face #"ETL (Extract #Transform #Load)" #Data Science #Docker #Azure #Security #Terraform #Infrastructure as Code (IaC) #AI (Artificial Intelligence) #Scala #Automation #Langchain #AWS (Amazon Web Services) #Model Deployment #GCP (Google Cloud Platform) #DevOps
Role description
Immediate opportunity for a Senior Machine Learning Engineer (Generative AI Focus) for a long term contract/potential right to hire opportunity.
This is a hybrid onsite (3 day) in Newark, NJ.
Seeking a highly skilled and experienced Senior Machine Learning Engineer to join our dynamic team. In the rapidly evolving world of Generative AI (GenAI). This role demands not only traditional machine learning expertise but also a deep understanding of GenAI-specific challenges. The ideal candidate will be a pivotal bridge between the theoretical capabilities of GenAI models and their practical application in production environments. We are looking for someone who can ensure our GenAI solutions are innovative, reliable, scalable, secure, and cost-effective.
Key Responsibilities:
β’ Model Deployment & Maintenance: Focus on deploying, monitoring, and maintaining GenAI models in production, ensuring they function reliably in real-world settings.
β’ Data Engineering: Build and maintain efficient data pipelines and storage solutions that support model operations.
β’ Infrastructure Management: Utilize cloud platforms (AWS, Azure, GCP) for model deployment, containerization (Docker), orchestration (Kubernetes), and infrastructure as code (Terraform/CloudFormation).
β’ DevOps & Automation: Develop CI/CD pipelines, manage version control (Git), and automate deployment processes for seamless operational efficiency.
β’ Security & Monitoring: Implement secure coding practices, authentication, authorization, and set up robust monitoring and alerting systems for both infrastructure and model performance.
β’ Generative AI Expertise: Deep understanding of LLMs, GenAI architectures, frameworks like Hugging Face, prompt engineering, and specialized infrastructure for GenAI workloads.
β’ Advanced Techniques: Apply advanced GenAI techniques like Retrieval-Augmented Generation (RAG), hallucination monitoring, and human-in-the-loop systems.
β’ Agent Development: Design and develop agent and multi-agent systems using frameworks like LangChain, enabling them to interact with external APIs and tools efficiently.
β’ Cost Optimization: Implement strategies to manage and reduce the operational costs associated with GenAI deployments.
Qualifications:
β’ Bachelor's degree in computer science/Engineering, data science, or a related field. Master's degree preferred
β’ At least 5years of experience as a machine learning engineer, deploying models in production
β’ Strong proficiency in Python and software engineering principles.
β’ Solid understanding of machine learning fundamentals and model lifecycle management.
β’ Experience with cloud platforms, containerization, and infrastructure management.
β’ Familiarity with DevOps practices and automation tools.
β’ Expertise in GenAI frameworks, prompt engineering, and model serving.
β’ Ability to manage GPU/TPU resources and optimize model serving frameworks.
β’ Experience in developing agentic systems and multi-agent architectures.
β’ Proven track record in cost optimization in AI deployments.
We are committed to attracting the best and brightest talent who are driven by impact and purpose. The Senior Machine Learning Engineer will play a crucial role in advancing our GenAI capabilities, pushing the boundaries of innovation while ensuring practical application and scalability. If you are passionate about transforming theoretical AI models into impactful real-world solutions, we invite you to join our team.
Immediate opportunity for a Senior Machine Learning Engineer (Generative AI Focus) for a long term contract/potential right to hire opportunity.
This is a hybrid onsite (3 day) in Newark, NJ.
Seeking a highly skilled and experienced Senior Machine Learning Engineer to join our dynamic team. In the rapidly evolving world of Generative AI (GenAI). This role demands not only traditional machine learning expertise but also a deep understanding of GenAI-specific challenges. The ideal candidate will be a pivotal bridge between the theoretical capabilities of GenAI models and their practical application in production environments. We are looking for someone who can ensure our GenAI solutions are innovative, reliable, scalable, secure, and cost-effective.
Key Responsibilities:
β’ Model Deployment & Maintenance: Focus on deploying, monitoring, and maintaining GenAI models in production, ensuring they function reliably in real-world settings.
β’ Data Engineering: Build and maintain efficient data pipelines and storage solutions that support model operations.
β’ Infrastructure Management: Utilize cloud platforms (AWS, Azure, GCP) for model deployment, containerization (Docker), orchestration (Kubernetes), and infrastructure as code (Terraform/CloudFormation).
β’ DevOps & Automation: Develop CI/CD pipelines, manage version control (Git), and automate deployment processes for seamless operational efficiency.
β’ Security & Monitoring: Implement secure coding practices, authentication, authorization, and set up robust monitoring and alerting systems for both infrastructure and model performance.
β’ Generative AI Expertise: Deep understanding of LLMs, GenAI architectures, frameworks like Hugging Face, prompt engineering, and specialized infrastructure for GenAI workloads.
β’ Advanced Techniques: Apply advanced GenAI techniques like Retrieval-Augmented Generation (RAG), hallucination monitoring, and human-in-the-loop systems.
β’ Agent Development: Design and develop agent and multi-agent systems using frameworks like LangChain, enabling them to interact with external APIs and tools efficiently.
β’ Cost Optimization: Implement strategies to manage and reduce the operational costs associated with GenAI deployments.
Qualifications:
β’ Bachelor's degree in computer science/Engineering, data science, or a related field. Master's degree preferred
β’ At least 5years of experience as a machine learning engineer, deploying models in production
β’ Strong proficiency in Python and software engineering principles.
β’ Solid understanding of machine learning fundamentals and model lifecycle management.
β’ Experience with cloud platforms, containerization, and infrastructure management.
β’ Familiarity with DevOps practices and automation tools.
β’ Expertise in GenAI frameworks, prompt engineering, and model serving.
β’ Ability to manage GPU/TPU resources and optimize model serving frameworks.
β’ Experience in developing agentic systems and multi-agent architectures.
β’ Proven track record in cost optimization in AI deployments.
We are committed to attracting the best and brightest talent who are driven by impact and purpose. The Senior Machine Learning Engineer will play a crucial role in advancing our GenAI capabilities, pushing the boundaries of innovation while ensuring practical application and scalability. If you are passionate about transforming theoretical AI models into impactful real-world solutions, we invite you to join our team.