Senior Machine Learning Ops Engineer

Steampunk • McLean, VA

47d • $140,000 - $190,000

About The Position

We are seeking a Senior ML Ops Engineer who specializes in assessing and developing AI/ML infrastructure, with a focus on Generative AI (Gen AI) and Retrieval-Augmented Generation (RAG) pipelines. In this role, you will lead efforts to build, optimize, and scale advanced AI/ML infrastructure that supports Federal use cases. The ideal candidate has a deep understanding of AI/ML systems and is passionate about developing production-grade pipelines that deliver results. You will contribute to the growth of our AI & Data Exploitation Practice!

Requirements

• Ability to hold a position of public trust with the US government.
• Master's degree in related program and 8 years of experience (7 of which must be relevant); OR Bachelor's degree in related program and 10 years of relevant experience; OR No degree and 16 years of relevant experience
• Possesses at least one professional certification relevant to the technical service provided. Maintain a certification relevant to the product being deployed and/or maintained.
• 7+ years of experience in AI/ML infrastructure assessment, development, and deployment, with a focus on production-grade pipeline development.
• Proven experience in building and scaling AI/ML pipelines, including generative AI models like GANs, VAEs, and Transformer-based architectures.
• Strong proficiency in Python and best practices for scalable and efficient coding; experience with R is a plus.
• Experience with AI/ML frameworks such as TensorFlow, PyTorch, Keras, or JAX and a solid understanding of neural network architectures.
• Expertise in cloud platforms (AWS, Azure, GCP) and experience with AI/ML tools like AWS SageMaker, Azure OpenAI, or similar.
• Practical experience with MLOps tools and frameworks, including automation of deployment, monitoring, and model management.
• Familiarity with DevSecOps practices to ensure secure and compliant deployment of AI/ML solutions.
• Strong knowledge of data pipeline tools and data visualization platforms, such as Tableau, Power BI, or D3.
• Experience with version control (Git), Bash, Unix commands, and cloud infrastructure automation tools.

Nice To Haves

• Familiarity with search technologies like Elasticsearch, AWS Kendra, or Azure Cognitive Search is a plus.

Responsibilities

• Assess and design AI/ML infrastructure to support scalable and secure deployment of machine learning models, including generative AI pipelines.
• Build, develop, and optimize Gen AI and RAG pipelines, ensuring seamless integration with existing systems and infrastructure.
• Evaluate and improve the performance, reliability, and scalability of AI/ML pipelines, identifying and addressing bottlenecks.
• Implement MLOps best practices for automating model deployment, monitoring, and retraining processes in production.
• Collaborate with Data Scientists and Software Engineers to transition models from research into production-grade pipelines.
• Continuously monitor AI/ML infrastructure and pipelines to ensure high performance, security, and compliance.
• Use cloud-native services (AWS, Azure, GCP) to deploy scalable and cost-effective solutions, including leveraging tools such as AWS SageMaker, Azure OpenAI, and others.
• Apply DevSecOps principles to maintain secure, reliable operations for AI/ML workflows, including CI/CD integration.
• Stay up-to-date on the latest research, trends, and tools in AI/ML, implementing cutting-edge technologies into infrastructure solutions.
• Contribute to the growth and innovation of our Data Exploitation Practice by delivering best-in-class AI/ML infrastructure solutions.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior