MLOps Engineer

MLOps Engineer (Contract Role) Job Title: MLOps Engineer Contract Duration: 6–12 Months (extension possible) Location: Remote (U.S.-based) Reports To: DevOps & Service Delivery Leadership Company Overview Our client is a rapidly growing technology services company specializing in AI, Data, and Quality Engineering solutions. With a global delivery model spanning North America, the United Kingdom, and an offshore development center in India, our client consistently delivers cutting-edge solutions that surpass expectations. The company has achieved triple-digit year-over-year growth and earned prestigious industry accolades, including making the Inc. 5000 list of fastest-growing companies and being recognized as Best IT Services Company, Best Data Technology Company, as well as Partner of the Year by Tricentis. They uphold a bold vision to be experts in AI, Data, and Quality Engineering transformations – encouraging innovative thinking and authentic relationships in all endeavors. Role Overview Our client is seeking an experienced MLOps Engineer to develop and operationalize a comprehensive AI cost tracking and observability framework across multiple cloud platforms. In this role, you will be instrumental in ensuring visibility into AI/ML model performance, usage, and cost metrics across Azure, Google Cloud, and Snowflake environments. You will collaborate closely with cross-functional teams (DevOps, FinOps, and others) to optimize model deployments both for performance and cost-efficiency. Key Responsibilities: • Cost & Observability Framework: Build a common AI cost tracking and observability framework spanning Azure ML, Google Vertex AI (Gemini), and Snowflake platforms. • Cloud Billing Integration: Integrate cloud billing and usage APIs (Azure ML, OpenAI, Google Vertex AI/Gemini) to aggregate and monitor AI service costs. • Metadata Tagging: Develop model-level metadata tagging processes for cost attribution and trend analysis, enabling granular visibility into costs per model or project. • Monitoring & Alerting: Implement and manage Datadog dashboards (or similar observability tools) with alerts for model performance issues – including latency spikes, model drift, and anomaly detection in predictions or usage. • Collaboration for Optimization: Work closely with DevOps and FinOps teams to visualize model costs and identify optimization opportunities (e.g. rightsizing resources, adjusting usage patterns). • Documentation & Knowledge Transfer: Deliver comprehensive documentation and conduct knowledge transfer sessions to internal teams at project closure, ensuring they can maintain and extend the cost tracking framework. Required Skills & Experience • MLOps/DevOps Experience: 5+ years of hands-on experience in MLOps, DevOps, or Cloud Engineering roles focused on AI/ML systems deployment and operations. • Cloud AI Platforms: Strong experience working with Azure ML, Google Vertex AI (Gemini), and OpenAI platforms/services, including deploying and managing models on these services. • Observability Tools: Expertise in Datadog (or equivalent monitoring/observability tools) for tracking application performance, logs, and metrics. • Programming & Automation: Advanced proficiency in Python and SQL for building automation scripts, data analysis, and integration of monitoring pipelines. • CI/CD & Monitoring Integration: Proven experience integrating cost and performance monitoring steps into CI/CD pipelines, ensuring that model deployments are coupled with automated observability and cost checks. • FinOps & Cost Management: Solid understanding of FinOps principles, cloud billing APIs, and strategies for cloud cost optimization in an engineering context (e.g. optimizing compute/storage for AI workloads). Preferred Qualifications • Generative AI Frameworks: Experience with GenAI/Agentic AI frameworks such as LangChain or building RAG (Retrieval-Augmented Generation) pipelines, especially in production environments. • Regulated Environment Experience: Familiarity with implementing cost tracking and ML monitoring in regulated environments (e.g. ensuring compliance with ISO, SOC 2, HITRUST or similar standards).