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Machine Learning Engineer Jobs in North Charleston, SC

Senior Software Engineer

Hanahan, SC · On-site

$53.35 - $88.03/hr

... machine learning, or enterprise application support. About PEMCCO PEMCCO supports complex technical ... Job Overview As a Senior Software Engineer, you will serve as a senior software engineering ...

Senior Software Engineer

Hanahan, SC · On-site

$53.35 - $88.03/hr

... machine learning, or enterprise application support. About PEMCCO PEMCCO supports complex technical ... Job Overview As a Senior Software Engineer, you will serve as a senior software engineering ...

Senior Software Engineer

Hanahan, SC · On-site

$53.35 - $88.03/hr

... machine learning, or enterprise application support. About PEMCCO PEMCCO supports complex technical ... Job Overview As a Senior Software Engineer, you will serve as a senior software engineering ...

... machine learning, or enterprise application support. About PEMCCO PEMCCO supports complex technical ... Job Overview As a Senior Software Engineer, you will serve as a senior software engineering ...

For those who want to keep growing, learning, and evolving. We at Kelly ® hear you, and we're here ... Programming, setting up, and operating a CNC Press Brake (preferably Amada), gantry style vertical ...

New

HII's diverse workforce includes skilled tradespeople; artificial intelligence, machine learning (AI/ML) experts; engineers; technologists; scientists; logistics experts; and business administration ...

Showing results 41-60

Machine Learning Engineer information

See North Charleston, SC salary details

$30.1K

$122.8K

$184.6K

How much do machine learning engineer jobs pay per year?

As of Aug 12, 2026, the average yearly pay for machine learning engineer in North Charleston, SC is $122,845.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,800.00 and $147,900.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are popular job titles related to Machine Learning Engineer jobs in North Charleston, SC? For Machine Learning Engineer jobs in North Charleston, SC, the most frequently searched job titles are:
What cities near North Charleston, SC are hiring for Machine Learning Engineer jobs? Cities near North Charleston, SC with the most Machine Learning Engineer job openings:
Infographic showing various Machine Learning Engineer job openings in North Charleston, SC as of August 2026, with employment types broken down into 79% Full Time, and 21% Contract. Highlights an 100% In-person job distribution, with an average salary of $122,845 per year, or $59.1 per hour.

Senior Data Scientist - Agentic AI & Multi-Cloud Architecture - with Security Clearance

Akima

North Charleston, SC • On-site

$64.25 - $85.75/hr

Other

Retirement

Posted 14 days ago


Akima rating

6.8

Company rating: 6.8 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

376th of 447 rated engineering


Job description

We are seeking an experienced Senior Data Scientist to support the Program Executive Office (PEO) Digital portfolio by leading the architecture, design, and implementation of next-generation Agentic AI capabilities for Department of Defense digital modernization initiatives. This individual will serve as the technical lead responsible for developing AI-enabled solutions that operate seamlessly across Microsoft Azure, AWS, Google Cloud Platform (GCP), and on-premises environments while maintaining strict security and compliance requirements for IL5 environments. This Hybrid position requires that you live within commuting distance from North Charleston, SC. Why Join Us This position offers the opportunity to shape the future of Artificial Intelligence across the PEO Digital portfolio by architecting enterprise-scale Agentic AI capabilities that support secure, multi-cloud operations across Azure, AWS, Google Cloud Platform, and on-premises environments. You will work alongside Government leaders, cloud architects, software engineers, cybersecurity professionals, and mission partners to deliver innovative AI solutions that accelerate digital modernization, improve mission effectiveness, and enable next-generation decision support for the Department of the Navy. To join our team of outstanding professionals, apply today! Responsibilities This role combines advanced data science, machine learning, AI orchestration, cloud architecture, and software engineering to build scalable, secure, and portable AI solutions capable of supporting mission-critical operations across the PEO Digital portfolio and multiple computing environments. The ideal candidate is equally comfortable discussing large language models with engineers, presenting AI architecture to senior Government leaders, and leading implementation teams through complex technical challenges.
AI Architecture & Strategy * Lead the design and implementation of enterprise Agentic AI solutions supporting PEO Digital modernization initiatives.
* Design portable AI architectures capable of operating across Azure, AWS, GCP, and on-premises environments.
* Evaluate technical feasibility of proposed AI capabilities and provide architectural recommendations.
* Develop scalable AI reference architectures that minimize vendor lock-in while maximizing deployment flexibility.
* Recommend emerging AI technologies and best practices supporting future mission requirements.Agentic AI Development Lead development of intelligent multi-agent systems including: * AI orchestration frameworks.
* Autonomous task planning.
* Tool execution.
* Agent collaboration.
* Workflow automation.
* Multi-agent reasoning.
* Retrieval-Augmented Generation (RAG).
* Enterprise knowledge management.
Experience with frameworks such as: * Semantic Kernel.
* AutoGen.
* LangGraph.
* LangChain.
* CrewAI.
* Similar agent orchestration platforms.Multi-Cloud & Hybrid Cloud Engineering Design and support AI deployments utilizing: * Microsoft Azure
* Azure Arc
* Azure Kubernetes Service (AKS)
* Amazon Web Services (AWS)
* Elastic Kubernetes Service (EKS)
* Google Cloud Platform (GCP)
* Google Kubernetes Engine (GKE)
* Hybrid Cloud architectures
* Edge computing environments
* On-premises Kubernetes deployments
* Develop cloud-agnostic deployment strategies supporting PEO Digital enterprise modernization objectives.Kubernetes & Container Platforms Lead containerized AI deployments utilizing: * Kubernetes.
* Azure Arc-enabled Kubernetes.
* Docker.
* Helm.
* GitOps.
* Infrastructure as Code.
* CI/CD pipelines.
Develop highly portable AI services capable of running in multiple classified and unclassified computing environments.
AI Model Deployment Design and deploy production AI inference environments utilizing technologies such as: * Hugging Face.
* vLLM.
* Text Generation Inference (TGI).
* Open-weight Large Language Models.
* Commercial AI services where authorized.
Optimize model performance, scalability, latency, and infrastructure utilization.
Data Science & Machine Learning Develop advanced analytics and machine learning solutions including: * Predictive analytics.
* NLP.
* Document intelligence.
* Semantic search.
* Embedding generation.
* Knowledge graph integration.
* AI-assisted decision support.
* Statistical modeling.
* Data mining.
* Feature engineering.Retrieval-Augmented Generation (RAG) Design enterprise RAG architectures utilizing: * Vector databases.
* pgvector.
* Milvus.
* Azure Arc-enabled PostgreSQL.
* Enterprise document repositories.
* Knowledge management systems.
Develop secure document interrogation capabilities supporting mission users.
Security & Compliance Design AI systems meeting DoD security requirements including: * Zero Trust Architecture.
* Microsoft Entra ID.
* Identity federation.
* Policy enforcement.
* Controlled Unclassified Information (CUI).
* IL5 environments.
* Audit logging.
* Data governance.
* AI governance.
Implement automated safeguards preventing ingestion or exposure of: * Personally Identifiable Information (PII).
* Protected Health Information (PHI).
Ensure AI outputs comply with applicable security marking and release requirements.
Technical Leadership * Lead AI technical strategy across multiple PEO Digital programs.
* Mentor junior data scientists, ML engineers, and software developers.
* Serve as technical advisor to Program Managers and Government stakeholders.
* Present architectural recommendations to executive leadership.
* Support proposal development and technical solutioning for new business opportunities. Qualifications * Bachelor's degree in computer science, Data Science, Artificial Intelligence, Engineering, Mathematics, or related technical discipline.
* Active Top Secret Clearance.
* 10+ years of professional experience in Data Science, Machine Learning, AI, or Cloud Engineering.
* 5+ years designing enterprise AI or ML solutions.
* Experience deploying AI solutions in cloud or hybrid-cloud environments.
* Experience with Kubernetes and containerized applications.
* Strong experience architecting multi-cloud AI solutions utilizing Azure, AWS, GCP, and Azure Arc.
* Experience building production machine learning pipelines.
* Strong Python programming skills.
* Experience working with REST APIs and microservices.
* Familiarity with Large Language Models and Generative AI.
* Excellent communication and technical presentation skills.
Preferred Qualifications: * Master's or Ph.D. in AI, Machine Learning, Computer Science, Data Science, Applied Mathematics, or related discipline.
* Experience supporting Program Executive Office (PEO) Digital, NIWC Atlantic, Marine Corps Systems Command, or other Department of Defense digital modernization organizations.
* Experience with Azure Arc.
* Experience with Azure AI Foundry.
* Experience with AWS Bedrock.
* Experience with Google Vertex AI.
* Experience deploying open-weight LLMs.
* Experience with Semantic Kernel, AutoGen, LangGraph, or similar orchestration frameworks.
* Experience implementing Retrieval-Augmented Generation (RAG).
* Experience with vector databases.
* Experience supporting Department of Defense customers.
* Experience supporting IL5 or classified computing environments.
* Active Secret Clearance or higher.
Preferred Certifications: * Microsoft Certified: Azure AI Engineer Associate.
* Microsoft Certified: Azure Solutions Architect Expert.
* AWS Certified Machine Learning - Specialty.
* Google Professional Machine Learning Engineer.
* Certified Kubernetes Administrator (CKA).
* Certified Kubernetes Application Developer (CKAD).
* Security+.
* PMP (preferred). Job ID 2026-24504 Work Type Hybrid Company Description Work Where it Matters Akima Systems Engineering (ASE), an Akima company, is not just another federal systems support contractor. As an Alaska Native Corporation (ANC), our mission and purpose extend beyond our exciting federal projects as we support our shareholder communities in Alaska. At ASE, the work you do every day makes a difference in the lives of our 15,000 Iñupiat shareholders, a group of Alaska natives from one of the most remote and harshest environments in the United States. For our shareholders, ASE provides support and employment opportunities and contributes to the survival of a culture that has thrived above the Arctic Circle for more than 10,000 years. For our government customers, ASE delivers solutions in maritime IT, systems engineering, and integration across the Department of Defense and stands ready to help improve operational performance at a reasonable and sustainable cost. As an ASE employee, you will be surrounded by a challenging, yet supportive work environment that is committed to innovation and diversity, two of our most important values. You will also have access to our comprehensive benefits and competitive pay in addition to growth opportunities and excellent retirement options.

What Akima employees say

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Benefits

Hours and flexibility

Workplace

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About Akima

Sourced by ZipRecruiter

As an Alaska Native Corporation headquartered in Herndon, Virginia, Akima is dedicated to delivering superior outcomes for our customers’ missions while simultaneously creating a long-lived asset for our Iñupiat shareholders. Akima maintains a portfolio of small businesses, 8(a) companies, and operating companies that deliver simplified and accelerated access to the products and services agencies need to ensure mission success.

Industry

Specialty trade contractors

Company size

5,001 - 10,000 Employees

Headquarters location

Herndon, VA, US

Year founded

1995

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