Forward Deployed Principal Engineer DataOps & MLOps

Title: Forward Deployed Principal Engineer DataOps & MLOps

Location: Fremont CA

Position Type: Full-Time (FTE)

Infogain is seeking a highly skilled Forward Deployed Principal Engineer to spearhead DataOps and MLOps transformations within a hyperscale consumer-tech client environment.

In this role you will operate at the critical intersection of platform engineering applied AI and client advisory. You will embed directly within client teams to architect build and deploy production-grade data and machine learning systems that support real-time high-volume products. The ideal candidate brings deep technical expertise thrives in ambiguity and possesses the unique ability to translate complex data and ML challenges into scalable business-impacting solutions.

We need a true builder-architect. You should be equally comfortable and effective at:

• Writing production-grade code and diving deep into the technical weeds.

• Designing large-scale resilient systems that can handle hyperscale traffic.

• Debating architectural trade-offs with senior engineers and stakeholders.

• Driving tangible outcomes in a fast-paced client-facing environment.

• Experience: 12 years of dedicated experience in Data Engineering ML Engineering or Platform Engineering.

• DataOps Expertise: Strong hands-on experience with tools like Airflow Prefect Apache Spark Kafka or Pub/Sub.

• MLOps Expertise: Proven ability to build and manage ML lifecycles using MLflow Kubeflow Vertex AI SageMaker or Azure ML.

• Programming Languages: High proficiency in Python (Scala or Java is a strong plus).

• Cloud-Native Architectures: Deep expertise in cloud environments with a strong preference for GCP (especially within Meta-like or high-scale environments).

• Containerization: Hands-on experience deploying and managing workloads using Kubernetes and Docker.

• Distributed Systems: A proven track record of working with high-scale highly available distributed systems.

• Experience working in Meta Google-scale or similarly complex tech environments.

• Exposure to GenAI and LLMOps including building RAG pipelines working with Vector Databases and prompt orchestration.

• Familiarity with Feature Stores (e.g. Feast Tecton) and real-time ML inference systems.

• Prior experience in a forward-deployed consulting or client-advisory role.