Principal Engineer - DataOps & MLOps

Principal Engineer - DataOps & MLOps (Burlingame, CA, 94011) | 05/15/26

Job Description Role:

 Forward Deployed Principal Engineer -

DataOps & MLOps Location:

 Burlingame, CA (Hybrid)

Type:

Full-time Job Description:

Infogain is seeking a Forward Deployed Principal Engineer to lead DataOps and MLOps transformations within a hyperscale, consumer-tech client environment. This role operates at the intersection of platform engineering, applied AI, and client advisory—embedding within client teams to build production-grade data and ML systems that support real-time, high-volume products. The ideal candidate brings deep technical expertise, thrives in ambiguity, and can translate complex data/ML challenges into scalable, business-impacting solutions. Why This Role Matters (Client Context) Massive data scale (billions of events/day; real-time + batch pipelines) Rapid experimentation cycles (A/B testing, model iteration at speed) High reliability expectations (low latency, high availability) Strong need for standardized, reusable ML platforms across teams Increasing shift toward AI-driven products and GenAI use cases Core Responsibilities 1. Embedded Engineering Leadership Work directly with client data science, product, and platform teams Translate product use cases into DataOps/MLOps architectures Lead design decisions for scalable, production-ready systems 2. DataOps Platform Enablement Build and optimize large-scale data pipelines (batch + streaming) Implement data quality, lineage, and observability frameworks Enable CI/CD for data workflows and analytics pipelines 3. MLOps & AI Lifecycle Management Design end-to-end ML pipelines (training ? deployment ? monitoring) Implement model versioning, experiment tracking, and reproducibility Operationalize real-time and batch model serving at scale 4. Cloud-Native Architecture & Scale Architect solutions on GCP/AWS/Azure aligned to client standards Leverage Kubernetes, distributed compute (Spark/Flink), and event systems Ensure performance, cost optimization, and reliability 5. Platform & Accelerator Development Build reusable frameworks for feature engineering, model deployment, and monitoring Standardize best practices across teams and use cases Contribute to Infogain accelerators (e.g., AI-enabled QA, data platforms) Must-Have Skills 12+ years in Data Engineering / ML Engineering / Platform Engineering Strong experience with:

DataOps:

Airflow/Prefect, Spark, Kafka/PubSub

MLOps:

MLflow, Kubeflow, Vertex AI / SageMaker / Azure ML Proficiency in Python (plus Scala/Java preferred) Deep expertise in cloud-native architectures (GCP preferred for Meta-like environments) Hands-on Kubernetes and containerization experience Experience with high-scale distributed systems Nice-to-Have Experience in Meta/Google-scale or similar environments Exposure to GenAI / LLMOps (RAG pipelines, vector DBs, prompt orchestration) Familiarity with feature stores (Feast, Tecton) and real-time inference systems Prior forward-deployed / consulting experience Success Metrics Reduction in model deployment cycle time (weeks ? days/hours) Improved pipeline reliability and data quality SLAs Scalable ML platform adoption across multiple teams Tangible business impact (e.g., improved engagement, conversion, or cost efficiency) Infogain Value Proposition Opportunity to work on cutting-edge AI + data platforms at hyperscale Direct engagement with top-tier clients (Meta, Microsoft, etc.) Ownership of end-to-end solutioning—not just advisory Ability to shape reusable IP and accelerators in AI/Data Profile We're Looking For A builder-architect who is equally comfortable: Writing production code Designing large-scale systems Debating trade-offs with senior engineers Driving outcomes in a client-facing environment Principal Engineer - DataOps & MLOps1AI Full TImeUnited States