Principal Engineer - DataOps & MLOps

Forward Deployed Principal Engineer - Data Ops & MLOps

Location:

Burlingame, CA (Hybrid)

Type:
Full-time

Infogain is seeking a Forward Deployed Principal Engineer to lead Data Ops 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.

• 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

1. Embedded Engineering Leadership

• Work directly with client data science, product, and platform teams
• Translate product use cases into Data Ops/MLOps architectures
• Lead design decisions for scalable, production-ready systems

2. Data Ops 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)

• 12+ years in Data Engineering / ML Engineering / Platform Engineering
• Strong experience with:
• Data Ops:
  Airflow/Prefect, Spark, Kafka/Pub Sub
• MLOps: MLflow, Kubeflow, Vertex AI / Sage Maker / 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

• 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

• 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)

• 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