Forward Deployed Principal Data Engineer DataOps/ Data/ ML / Platform-12 years --Full time Hire--ONSITE

Full Time Hire requirement  
Forward Deployed Principal Engineer DataOps (Data Operations) & MLOps (Machine Learning Operations) 
Location                     :          Fremont, CA - Hybrid 4 Days Onsite (local Bay Area preferred)
 About the Role

• We are 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.

• This is a highly technical, hands-on leadership role focused on building and scaling data platforms, machine learning platforms, and cloud-native infrastructure that support AI and ML products in production.

• The ideal candidate is not just someone who builds machine learning models, but someone who has built the systems, pipelines, deployment frameworks, and infrastructure required to run those models reliably at scale.

• The Forward Deployed Principal Engineer builds the platform and infrastructure that allows AI applications and models to operate reliably at scale.

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 (Google Cloud Platform 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)

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