Title: Forward Deployed Principal Engineer DataOps & MLOps
Location: Fremont, CA
Position Type: Full-Time (FTE)
The Opportunity
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.
The Profile We're Looking For
We need a true builder-architect. You should be equally comfortable and effective at:
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Writing production-grade code and diving deep into the technical weeds.
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Designing large-scale, resilient systems that can handle hyperscale traffic.
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Debating architectural trade-offs with senior engineers and stakeholders.
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Driving tangible outcomes in a fast-paced, client-facing environment.
Must-Have Qualifications
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Experience: 12+ years of dedicated experience in Data Engineering, ML Engineering, or Platform Engineering.
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DataOps Expertise: Strong, hands-on experience with tools like Airflow, Prefect, Apache Spark, Kafka, or Pub/Sub.
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MLOps Expertise: Proven ability to build and manage ML lifecycles using MLflow, Kubeflow, Vertex AI, SageMaker, or Azure ML.
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Programming Languages: High proficiency in Python (Scala or Java is a strong plus).
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Cloud-Native Architectures: Deep expertise in cloud environments, with a strong preference for GCP (especially within Meta-like or high-scale environments).
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Containerization: Hands-on experience deploying and managing workloads using Kubernetes and Docker.
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Distributed Systems: A proven track record of working with high-scale, highly available distributed systems.
Nice-to-Have (Bonus Points)
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Experience working in Meta, Google-scale, or similarly complex tech environments.
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Exposure to GenAI and LLMOps, including building RAG pipelines, working with Vector Databases, and prompt orchestration.
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Familiarity with Feature Stores (e.g., Feast, Tecton) and real-time ML inference systems.
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Prior experience in a forward-deployed, consulting, or client-advisory role.
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