DevOps MLOps Engineer (Python), Legal Operations

DevOps MLOps Engineer (Python), Legal Operations Day 1 onsite Cupertino, CA / Austin, TX (Prefers local folks) Hybrid – 3 days onsite and 2 days remote Long term contract Direct client opportunity No mid layer / No Implementation partners are Involved For this role, ideally someone has previous working exp with same client, because this role is specific to how we deploy solutions/ products/ code in Client environment. • The Applied Data Science team within Legal Operations builds AI-powered tools, analytics capabilities, and data infrastructure that enable Client’s legal organization to work smarter and faster. • The team uses Client’s internal AI platform to build and deploy production-grade AI applications — from document analysis and legal research to spend analytics and conversational intelligence. • As the volume and complexity of AI deployments grows, the team needs a dedicated engineering discipline to own the path from prototype to production. • The MLOps Engineer is the bridge between the AI applications the Applied Data Science team builds and the production environment where stakeholders rely on them. • You will own deployment pipelines, integration infrastructure, access governance, and scalability for every AI-powered tool the team ships. • This is not a model training or data science role — it is an engineering role focused on making AI applications reliable, governed, and scalable in an enterprise environment. Key Responsibilities Deployment & Delivery • Own CI/CD pipelines for AI-powered applications — from development environments through production release • Build and maintain reusable deployment templates that enable the team to ship AI tools faster and more consistently • Manage environment promotion — dev → staging → production — with appropriate testing and validation gates at each stage • Coordinate with infrastructure and platform teams on deployment standards and security requirements Integration & Data Connectivity • Own API integrations connecting AI applications to live Legal data sources • Manage live data refresh pipelines, ensuring AI tools reflect current data without manual intervention • Version and manage API contracts — handling changes in upstream data sources without breaking downstream AI applications • Troubleshoot and resolve integration failures with minimal impact to end users Governance & Security • Implement role-based access control (RBAC) for all deployed AI applications — ensuring the right stakeholders have access to the right tools • Build and maintain audit logging — capturing usage, queries, and responses for compliance and accountability • Ensure all deployments meet Client's enterprise security standards — secrets management, authentication, data handling policies • Partner with governance and data teams to enforce data access policies at the application layer Observability & Reliability • Instrument deployed AI applications with monitoring for latency, error rates, token usage, and response quality • Build alerting for production failures and performance degradation • Establish SLAs for AI application uptime and response time; own resolution when they are breached • Maintain deployment documentation and runbooks for every production application Enablement • Reduce the deployment burden on data scientists and AI engineers — abstract complexity so builders can focus on building • Build and maintain developer tooling that makes the path from prototype to production faster and more self-service over time Minimum Qualifications • 3+ years of experience in MLOps, DevOps, or platform engineering roles • Strong Python skills — this is the primary language for tooling and automation • Hands-on experience deploying and operating LLM-powered applications in production • Experience building CI/CD pipelines for AI or software applications (GitHub Actions or equivalent) • Experience with REST API development and integration — connecting applications to live data sources • Working knowledge of containerization — Docker; Kubernetes basics • Experience implementing access control, authentication, and audit logging in enterprise environments Preferred Qualifications • Exp with LLM observability tooling — monitoring latency, token usage, response quality in production • Familiarity with dbt, Snowflake, or enterprise data warehouse environments • Exp with vector database management — Chroma, Pinecone, Weaviate, or equivalent • Exp in a regulated or compliance-sensitive environment where auditability and data access governance are non-negotiable • Familiarity with RAG (Retrieval-Augmented Generation) application architecture — not to build them, but to deploy and operate them reliably. Pay Range: $70/hr - $75/hr The specific compensation for this position will be determined by a number of factors, including the scope, complexity and location of the role as well as the cost of labor in the market; the skills, education, training, credentials and experience of the candidate; and other conditions of employment. Our full-time consultants have access to benefits including medical, dental, and vision as well as 401K contributions.