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Ml Engineer Jobs Near Me

ML Engineer (AI/LLM & Cloud) Location: Jersey City, NJ (Hybrid - 3 Days Onsite) OR Columbus, OH (5 Days Onsite) Duration: Contract-to-Hire Job Summary We are seeking a highly experienced ML Engineer ...

ML Engineering role focused on building AI solutions and integrating them into the existing JPMorgan Chase ecosystem. * Work closely with Data Science teams and Subject Matter Experts (SMEs) to ...

AI/ML Engineer

Columbus, OH · On-site

$120K - $130K/yr

Job Title AI/ML Engineer Key Responsibilities * Build and operate production agentic AI systems, including deployed, monitored, and debugged agents running at scale. Build and operate production ...

Senior ML Ops Engineer

Columbus, OH

$100K - $138K/yr

Senior ML Ops Engineer Overview As a Senior ML Ops Engineer at Mimecast, you will be a technical leader on the AI Enablement Platform (AIP) team, responsible for ensuring that machine learning models ...

Senior ML Ops Engineer

Columbus, OH · On-site

$100K - $138K/yr

Senior ML Ops Engineer Overview As a Senior ML Ops Engineer at Mimecast, you will be a technical leader on the AI Enablement Platform (AIP) team, responsible for ensuring that machine learning models ...

Senior ML Ops Engineer

Columbus, OH · On-site

$148 - $222/hr

As a Senior ML Ops Engineer at Mimecast, you will be a technical leader on the AI Enablement Platform (AIP) team, responsible for ensuring that machine learning models and AI agents are deployed ...

Senior AI Machine Learning Engineer

Columbus, OH · On-site

$118K - $156K/yr

As a Senior AI/ML engineer youwill manage and modernize the existing predictive model portfolio while helping the team expand into generative AI, agentic AIandother applied AI capabilities. The role ...

Knowledge of ML engineering core tasks and techniques, such as data and optimization pipelines, model deployment, and MLOps. Equal Opportunity Statement We are an Equal Opportunity, Affymative Action ...

Google Senior Data Engineer

Hartford, OH · On-site

$94K - $266K/yr

Implement ML pipelines and help establish MLOps processes (monitoring, retraining, deployment). * Support prompt engineering, embeddings, and retrieval-augmented generation (RAG) experimentation.

Google Senior Data Engineer

Columbus, OH · On-site

$94K - $266K/yr

Implement ML pipelines and help establish MLOps processes (monitoring, retraining, deployment). * Support prompt engineering, embeddings, and retrieval-augmented generation (RAG) experimentation.

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How much do ml engineer jobs pay per year?

As of Aug 20, 2026, the average yearly pay for ml engineer in the United States is $89,183.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,500.00 and $109,000.00 per year, depending on experience, location, and employer.

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A map of the United States highlighting the number of Ml Engineer job openings by state according to ZipRecruiter. The image is accompanied by a detailed chart listing the number of Ml Engineer job openings in each state, with California having the most at 2 and Hawaii the least at 0.

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Posted 27 days ago


Job description

Job Title: ML Engineer (AI/LLM & Cloud)

Location: Jersey City, NJ (Hybrid – 3 Days Onsite) OR Columbus, OH (5 Days Onsite)
Duration: Contract-to-Hire


Job Summary

We are seeking a highly experienced ML Engineer to design, build, deploy, and integrate enterprise-scale AI/ML solutions within the JPMorgan Chase ecosystem. This role focuses on engineering production-ready machine learning applications, Large Language Model (LLM) solutions, and cloud-native ML platforms while partnering closely with Data Science teams and business stakeholders.

The ideal candidate is a hands-on engineer with strong Python expertise, cloud experience, and MLOps knowledge who can lead technical initiatives and deliver scalable AI solutions.


Key Responsibilities
  • Design, develop, deploy, and maintain scalable AI/ML applications.
  • Build production-ready machine learning systems and infrastructure.
  • Productionize machine learning models developed by Data Science teams.
  • Design and deploy Large Language Model (LLM) applications.
  • Integrate AI solutions into AWS and JPMorgan Chase internal cloud platforms.
  • Develop scalable model serving and inference pipelines.
  • Implement CI/CD pipelines for ML applications.
  • Build monitoring, observability, model drift detection, and automated retraining solutions.
  • Optimize AI applications for performance, scalability, reliability, and cost.
  • Collaborate with Product Managers, Data Scientists, Software Engineers, and Business SMEs.
  • Mentor junior engineers and provide technical leadership through architecture guidance and code reviews.
  • Research and implement modern AI/ML technologies and best practices.

Required Qualifications
  • Bachelor''s or Master''s degree in Computer Science, Engineering, Data Science, or a related field.
  • 10+ years of hands-on experience developing and deploying machine learning solutions in production.
  • Expert-level programming experience with Python.
  • Basic understanding of Java or Scala.
  • Strong experience with software engineering principles, data structures, algorithms, and distributed systems.
  • Extensive experience with AWS Cloud.
  • Experience deploying applications across public cloud and enterprise cloud platforms.
  • Hands-on experience with Docker and Kubernetes.
  • Strong experience with MLOps tools including MLflow, Kubeflow, SageMaker, or Vertex AI.
  • Experience implementing CI/CD pipelines for machine learning applications.
  • Experience designing scalable ML infrastructure and distributed data processing systems.
  • Proven ability to lead technical initiatives and mentor engineering teams.

Preferred Qualifications
  • Experience building and deploying Large Language Model (LLM) solutions.
  • Hands-on experience with Amazon Bedrock.
  • Experience with Generative AI applications.
  • Strong understanding of TensorFlow, PyTorch, or Scikit-learn.
  • Experience with Deep Learning, NLP, or Computer Vision.
  • Experience with distributed model training and high-throughput inference systems.
  • Knowledge of model optimization and AI performance tuning.