Senior Machine Learning Engineer; Generative AI​/MLOps

Overview

Work Type:
Contract-to-hire

Pay Rate: $70–$90/hour on W2 (based on experience)

No C2C

We are seeking a Senior Machine Learning Engineer with a strong Generative AI and MLOps focus to join a financial services organization on a contract-to-hire basis. This role is highly hands-on and sits at the intersection of machine learning, cloud infrastructure, Dev Ops, and production engineering
, with a strong emphasis on deploying and operating Generative AI solutions at scale
.

The ideal candidate is someone who thrives in production environments, understands the realities of deploying GenAI models, and can bridge advanced ML concepts with reliable, secure, and cost-effective real-world implementations.

• Production ML & Generative AI
  :
  Deploy, monitor, and maintain Generative AI and machine learning models in production environments;
  Ensure reliability, scalability, security, and performance of GenAI solutions in real-world use cases;
  Apply advanced GenAI techniques including LLMs, Retrieval-Augmented Generation (RAG), hallucination monitoring, and human-in-the-loop workflows
• Infrastructure & MLOps
  :
  Design and manage ML infrastructure using cloud platforms (AWS, Azure, or GCP);
  Build and maintain containerized workloads using Docker and orchestration with Kubernetes;
  Implement infrastructure-as-code using Terraform or Cloud Formation;
  Develop and manage CI/CD pipelines to support model development, deployment, and versioning;
  Build and maintain data pipelines and storage solutions that support model training and inference;
  Partner with data engineers to ensure efficient, reliable data access for ML workflows
• Security, Monitoring & Cost Optimization
  :
  Implement secure coding practices, authentication, and authorization for ML systems;
  Set up monitoring, alerting, and logging for infrastructure and model performance;
  Manage GPU/TPU resources and optimize model serving to control operational costs
• Agent & System Design
  :
  Design and implement agentic and multi-agent systems using frameworks such as Lang Chain;
  Enable GenAI systems to interact with external APIs, tools, and services effectively

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field (Master’s preferred)
• 5+ years of experience as a Machine Learning Engineer with hands-on production deployment experience
• Strong proficiency in Python and modern software engineering best practices
• Solid understanding of machine learning fundamentals, model lifecycle management, and production monitoring
• Experience with cloud platforms, containerization, and infrastructure management
• Hands-on experience with Dev Ops practices, CI/CD pipelines, and automation tools
• Demonstrated experience working with Generative AI frameworks, prompt engineering, and model serving
• Ability to work independently in a fast-paced environment

• Experience managing GPU/TPU workloads and optimizing inference performance
• Hands-on experience with agent frameworks and multi-agent architectures
• Proven ability to optimize costs associated with GenAI deployments
• Experience deploying ML solutions in regulated or enterprise environments

• Work on real-world Generative AI systems, not prototypes
• Own the full lifecycle from model deployment to production stability
• Influence how GenAI is operationalized in a financial services environment
• Opportunity to convert from contract to full-time based on performance