Forward Deployed Principal Engineer - DataOps & MLOpsLocation: Burlingame, CA (Hybrid)Type: Full-timeInfogain 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 teamsIncreasing shift toward AI-driven products and GenAI use casesCore Responsibilities1. Embedded Engineering LeadershipWork directly with client data science, product, and platform teamsTranslate product use cases into DataOps/MLOps architecturesLead design decisions for scalable, production-ready systems2.
DataOps Platform EnablementBuild and optimize large-scale data pipelines (batch + streaming)Implement data quality, lineage, and observability frameworksEnable CI/CD for data workflows and analytics pipelines3. MLOps & AI Lifecycle ManagementDesign end-to-end ML pipelines (training? deployment?
monitoring)Implement model versioning, experiment tracking, and reproducibilityOperationalize real-time and batch model serving at scale4. Cloud-Native Architecture & ScaleArchitect solutions on GCP/AWS/Azure aligned to client standardsLeverage Kubernetes, distributed compute (Spark/Flink), and event systemsEnsure performance, cost optimization, and reliability5. Platform & Accelerator DevelopmentBuild reusable frameworks for feature engineering, model deployment, and monitoringStandardize best practices across teams and use casesContribute to Infogain accelerators (e.g., AI-enabled QA, data platforms)Must-Have Skills12+ years in Data Engineering / ML Engineering / Platform EngineeringStrong experience with:DataOps: Airflow/Prefect, Spark, Kafka/PubSubMLOps: MLflow, Kubeflow, Vertex AI / SageMaker / Azure MLProficiency in Python (plus Scala/Java preferred)Deep expertise in cloud-native architectures (GCP preferred for Meta-like environments)Hands-on Kubernetes and containerization experienceExperience with high-scale distributed systemsNice-to-HaveExperience in Meta/Google-scale or similar environmentsExposure to GenAI / LLMOps (RAG pipelines, vector DBs, prompt orchestration)Familiarity with feature stores (Feast, Tecton) and real-time inference systemsPrior forward-deployed / consulting experienceSuccess MetricsReduction in model deployment cycle time (weeks?
days/hours)Improved pipeline reliability and data quality SLAsScalable ML platform adoption across multiple teamsTangible business impact (e.g., improved engagement, conversion, or cost efficiency)Infogain Value PropositionOpportunity to work on cutting-edge AI + data platforms at hyperscaleDirect engagement with top-tier clients (Meta, Microsoft, etc.)Ownership of end-to-end solutioning—not just advisoryAbility to shape reusable IP and accelerators in AI/DataProfile We’re Looking ForA builder-architect who is equally comfortable:Writing production codeDesigning large-scale systemsDebating trade-offs with senior engineers