Senior Machine Learning Engineer (Generative AI / MLOps)

Senior Machine Learning Engineer (Generative AI / MLOps) Location: Contract-to-Hire | Financial Services 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, DevOps, 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. Key Responsibilities 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 CloudFormation • Develop and manage CI/CD pipelines to support model development, deployment, and versioning Data Engineering & Pipelines • 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 LangChain • Enable GenAI systems to interact with external APIs, tools, and services effectively Required Qualifications • 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 DevOps 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 Highly Desired Skills • 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 Why This Role • 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