Overview
Remote
On Site
Contract - W2
Skills
Optimization
Training
Testing
Workflow
Use Cases
Collaboration
Management
IaaS
Documentation
Software Engineering
Data Engineering
DevOps
FOCUS
Machine Learning Operations (ML Ops)
Python
Writing
Lifecycle Management
Docker
Kubernetes
Orchestration
Generative Artificial Intelligence (AI)
LangChain
Continuous Delivery
GitLab
Continuous Integration
Jenkins
Cloud Computing
Amazon SageMaker
Google Cloud
Google Cloud Platform
Vertex
Artificial Intelligence
Microsoft Azure
Machine Learning (ML)
Banking
Financial Services
Conflict Resolution
Problem Solving
Communication
Stakeholder Management
Job Details
Overview
Our client, a leading organization in the banking sector, is seeking a Senior MLOps Consultant with deep technical expertise in Machine Learning Operations (MLOps) and Generative AI (GenAI) to support the design, deployment, and optimization of scalable ML solutions. This is a coding-intensive, hands-on consulting role that requires strong proficiency in containerization technologies such as Docker and Kubernetes (K8s), and experience working within secure enterprise environments.
Key Responsibilities
Our client, a leading organization in the banking sector, is seeking a Senior MLOps Consultant with deep technical expertise in Machine Learning Operations (MLOps) and Generative AI (GenAI) to support the design, deployment, and optimization of scalable ML solutions. This is a coding-intensive, hands-on consulting role that requires strong proficiency in containerization technologies such as Docker and Kubernetes (K8s), and experience working within secure enterprise environments.
Key Responsibilities
- Design, implement, and optimize end-to-end MLOps pipelines for training, testing, deploying, and monitoring ML/AI models.
- Build and maintain scalable CI/CD processes for ML workflows, ensuring automation and reproducibility.
- Develop and deploy GenAI solutions and frameworks to support business use cases.
- Collaborate with Data Scientists, Data Engineers, and Cloud Architects to ensure reliable and secure model operations.
- Manage containerized ML workloads using Docker and Kubernetes, ensuring optimal performance and resource utilization.
- Integrate ML pipelines with cloud infrastructure and enterprise data systems.
- Troubleshoot, tune, and improve model serving and monitoring processes.
- Contribute to best practices, documentation, and governance standards across the ML lifecycle.
- 7+ years of hands-on experience in software engineering, data engineering, or DevOps with a focus on MLOps.
- Strong proficiency in Python (coding heavy role - must be comfortable writing and optimizing production-grade code).
- Solid understanding of ML lifecycle management, model versioning, and reproducibility principles.
- Expertise with Docker, Kubernetes, and container orchestration for ML workloads.
- Practical experience with GenAI frameworks (e.g., LangChain, Hugging Face, OpenAI APIs, etc.).
- Familiarity with CI/CD tools (GitLab CI, Jenkins, ArgoCD, etc.) and cloud ML platforms (AWS Sagemaker, Google Cloud Platform Vertex AI, or Azure ML).
- Previous experience working in the banking or financial services sector is highly desirable.
- Excellent problem-solving, communication, and stakeholder-management skills.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.