ZipRecruiter

Log In

1

Mlops Jobs in Delaware (NOW HIRING)

ClifyX

GenAI Engineer

Wilmington, DE · On-site

Agile/Scrum, MLOps (CI/CD, Model Versioning, Deployment) • Compliance: Banking regulations (SR 11-7, GDPR), Model Risk Management • Soft Skills: Strong communication, stakeholder management, and ...

Elevance Health

Gen AI Engineer

Wilmington, DE · Hybrid

Work closely with the MLOps team to create and maintain robust evaluation solutions and tools to evaluate model performance, accuracy, consistency, reliability, during development, and UAT.

Elevance Health

Gen AI Engineer

Wilmington, DE · Hybrid

Work closely with the MLOps team to create and maintain robust evaluation solutions and tools to evaluate model performance, accuracy, consistency, reliability, during development, and UAT.

next page

Showing results 1-20

All JobsMlops JobsMlops Jobs in Delaware

Mlops information

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

What are the key skills and qualifications needed to thrive as an MLOps Engineer, and why are they important?

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

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

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What are MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.
What are the most commonly searched types of Mlops jobs in Delaware? The most popular types of Mlops jobs in Delaware are:
What are popular job titles related to Mlops jobs in Delaware? For Mlops jobs in Delaware, the most frequently searched job titles are:
What job categories do people searching Mlops jobs in Delaware look for? The top searched job categories for Mlops jobs in Delaware are:
What cities in Delaware are hiring for Mlops jobs? Cities in Delaware with the most Mlops job openings:
Mlops jobs near you
Infographic showing various Mlops job openings in Delaware as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

GenAI Engineer

Tata Consultancy Service Limited

Wilmington, DE • On-site

$100K - $110K/yr

Full-time

Posted 8 days ago

Job description

GenAI Engineer
Design and develop AI/ML and Generative AI solutions for banking use cases including fraud detection, risk modeling, and customer analytics.
• Build, fine-tune, and deploy ML models and LLMs for credit scoring, AML, and automation
• Implement RAG-based GenAI applications using internal banking data
• Develop scalable data pipelines for training, validation, and real-time inference
• Collaborate with risk, compliance, finance, and business teams for AI solutions
• Ensure regulatory compliance and AI governance standards
• Implement data security, privacy, and access control mechanisms
• Integrate AI models into production using APIs and microservices
• Apply prompt engineering and model optimization techniques
• Monitor model performance, drift detection, and continuous improvement
• Develop explainable AI (XAI) for transparent decision-making
• Optimize cost, latency, and scalability of AI systems
• Troubleshoot AI/ML system issues across data and deployment layers
• Write efficient Python code using AI frameworks
• Follow MLOps best practices (CI/CD, automated deployment)
• Ensure responsible AI practices (bias, fairness, ethics)
• Mentor teams and contribute to enterprise AI platforms.
• Languages: Python
• AI/ML & GenAI: Machine Learning, Deep Learning, LLMs, Prompt Engineering, Fine-tuning
• Frameworks: TensorFlow, PyTorch
• GenAI Tools: LangChain, LlamaIndex
• Vector DB: Pinecone, FAISS
• Cloud Technologies: AWS / Azure / GCP
• Data Pipelines: ETL/ELT, Real-time & Batch Processing
• Integration: APIs, Microservices
• Concepts: RAG Architecture, XAI, Model Optimization
• Methodologies: Agile/Scrum, MLOps (CI/CD, Model Versioning, Deployment)
• Compliance: Banking regulations (SR 11-7, GDPR), Model Risk Management
• Soft Skills: Strong communication, stakeholder management, and analytical thinking
Salary Range- $100,000-$110,000 a year
#LI-SP3
#LI-VX1

Apply