Overview
Remote
On Site
Contract - W2
Skills
Banking
Software Engineering
Training
Testing
Workflow
Scalability
Collaboration
Data Science
Cloud Architecture
Quality Management
Orchestration
Amazon Web Services
Documentation
Machine Learning Operations (ML Ops)
DevOps
Data Engineering
FOCUS
Quality Assurance
Management
Shipping
Python
Lifecycle Management
Docker
Kubernetes
Continuous Delivery
GitLab
Continuous Integration
Jenkins
Generative Artificial Intelligence (AI)
LangChain
Use Cases
Cloud Computing
Amazon SageMaker
Google Cloud
Google Cloud Platform
Vertex
Artificial Intelligence
Microsoft Azure
Machine Learning (ML)
Communication
Analytical Skill
Conflict Resolution
Problem Solving
Financial Services
Job Details
Senior MLOps Consultant (Agentic AI Engineering Delivery)
Overview
Our client, a leading organization in the banking sector, is seeking a Senior MLOps Consultant with proven, hands-on experience building and deploying production-grade Agentic AI systems. This role is deeply technical, requiring strong software engineering and platform delivery expertise rather than QA or oversight. The ideal candidate has built and shipped complex ML/AI systems leveraging MLOps best practices and modern GenAI frameworks.
This consultant will be instrumental in designing, automating, and scaling ML and GenAI workloads within secure, enterprise-grade environments-combining MLOps engineering, Agentic AI development, and platform reliability into one cohesive delivery function.
Key Responsibilities
Overview
Our client, a leading organization in the banking sector, is seeking a Senior MLOps Consultant with proven, hands-on experience building and deploying production-grade Agentic AI systems. This role is deeply technical, requiring strong software engineering and platform delivery expertise rather than QA or oversight. The ideal candidate has built and shipped complex ML/AI systems leveraging MLOps best practices and modern GenAI frameworks.
This consultant will be instrumental in designing, automating, and scaling ML and GenAI workloads within secure, enterprise-grade environments-combining MLOps engineering, Agentic AI development, and platform reliability into one cohesive delivery function.
Key Responsibilities
- Design, implement, and optimize end-to-end MLOps pipelines for model training, testing, deployment, and monitoring.
- Build, ship, and operationalize Agentic AI systems and large-scale ML workflows with a focus on automation, scalability, and reliability.
- Develop and maintain robust platform engineering solutions to support high-performance ML/GenAI workloads.
- Collaborate with cross-functional teams (Data Science, Engineering, Cloud Architecture) to deliver secure and high-quality systems.
- Manage containerized AI workloads using Docker and Kubernetes for efficient orchestration and scaling.
- Integrate ML pipelines into cloud and enterprise data ecosystems (AWS, Azure, or Google Cloud Platform).
- Continuously improve system observability, model performance, and deployment automation.
- Contribute to engineering best practices, reusable frameworks, and documentation to enable delivery excellence.
- 7+ years of hands-on engineering experience across MLOps, DevOps, or Data Engineering with a strong focus on delivery and system build (not QA or management).
- Proven track record building and shipping Agentic AI or ML systems in production environments.
- Expert-level Python programming skills for production-grade system development.
- Deep knowledge of ML lifecycle management, including model versioning, monitoring, and governance.
- Strong expertise in Docker, Kubernetes, and related CI/CD tooling (GitLab CI, Jenkins, ArgoCD, etc.).
- Experience implementing or extending GenAI frameworks (LangChain, Hugging Face, OpenAI APIs, etc.) for enterprise use cases.
- Solid understanding of cloud-native ML platforms such as AWS SageMaker, Google Cloud Platform Vertex AI, or Azure ML.
- Strong communication, analytical, and problem-solving abilities, with experience in financial services or secure enterprise environments preferred.
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.