Architect - Platform Engineering - USA

About the Company

Quantiphi is an award-winning, AI-First digital engineering and consulting company focused on delivering high-impact services and solutions that help organizations solve what truly matters. We partner with enterprises to reimagine their businesses through intelligent, scalable, and transformative AI driving measurable outcomes at the core of their operations. Since our founding in 2013, we’ve tackled complex business challenges by combining deep industry expertise, disciplined cloud and data engineering practices, and cutting-edge applied AI research.

Headquartered in Boston, Quantiphi is a global organization with 4,000+ professionals serving clients across key industry verticals, including BFSI, Healthcare & Life Sciences, CPG, Manufacturing, and TME. As an Elite and Premier partner to leading cloud and AI platforms such as NVIDIA, Google Cloud, AWS, and Snowflake, we build and deliver enterprise-grade AI services and solutions that create real-world impact.

We’ve been recognized with 21x Google Cloud Partner of the Year awards, 3x AWS AI/ML award wins, 3x NVIDIA Partner of the Year titles, and 2x Snowflake Partner of the Year awards. We’ve also garnered top analyst recognitions from Gartner, ISG, and Everest Group and have been certified as a Great Place to Work for three consecutive years (2021, 2022, 2023).

About the Role

Be part of a trailblazing team shaping the future of AI, ML, and cloud innovation. This role is based in Chicago, IL, or surrounding states, with up to 30% travel required.

Key Skills

• 10+ years working in ML/AI platform engineering or AI/MLOps roles with strong architecture exposure.
• Strong expertise in the Google Cloud (GCP) native AI/ML stack, including: Vertex AI (primary), Google Kubernetes Engine (GKE), Cloud Functions, AutoML, Vertex AI Pipelines, BigQuery ML, API Gateway, and CI/CD (Cloud Build/Cloud Deploy or equivalent).
• Hands-on experience with MLOps toolset and awareness of: MLflow, Kubeflow, Vertex AI Pipelines, Airflow, BentoML, KServe, Seldon.
• Deep understanding of model lifecycle management (feature engineering → training → registry → deployment → monitoring).
• Experience implementing or supporting LLMOps pipelines, including prompt versioning, evaluation metrics, and automation frameworks.
• Deep understanding of the ML lifecycle: data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance.
• Strong experience with Google Cloud's Vertex AI platform, including Pipelines, Feature Store, Model Registry, and Model Monitoring.
• Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
• Strong SQL and data transformation experience using Snowflake, Databricks, Spark.
• Experience with feature engineering pipelines and Feature Store management.
• Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, and reproducibility.
• Hands-on experience with Vertex AI Foundation Models, OpenAI, Anthropic, or Llama models.
• Experience with Cloud Monitoring, Vertex AI Model Monitoring, Prometheus/Grafana.
• Strong foundation in Python and cloud-native development patterns.
• Solid understanding of security best practices, Cloud IAM, secrets management, and artifact governance.

Responsibilities

• Architect and implement the MLOps strategy for the program, ensuring alignment with the project proposal and delivery roadmap.
• Design and own enterprise-grade ML/LLM pipelines covering model training, validation, deployment, versioning, monitoring, and CI/CD automation using GCP-native services.
• Build container-oriented ML platforms (GKE-first) while evaluating alternative orchestration tools with similar capabilities (Kubeflow, Vertex AI, MLflow, Airflow, etc.).
• Implement hybrid MLOps + LLMOps workflows, including prompt/version governance, evaluation frameworks, and monitoring for LLM-based systems within the GCP environment.
• Serve as a technical authority across multiple internal and customer projects, contributing architectural patterns, best practices, and reusable frameworks for GCP.
• Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems using tools like Cloud Monitoring and Vertex AI Model Monitoring.
• Collaborate with cross-functional teams — data engineering, platform, DevOps, and client stakeholders — to deliver production-ready ML solutions on Google Cloud.
• Ensure all solutions adhere to security, governance, and compliance expectations, particularly around handling GCP services, Google Kubernetes Engine workloads, and MLOps tools.
• Conduct architecture reviews, troubleshoot complex ML system issues, and guide teams through implementation across cloud-native ML platforms on GCP.
• Mentor engineers and provide guidance on modern MLOps tools, Vertex AI platform capabilities, and best practices.

Benefits

• Be part of the fastest-growing AI-first digital transformation and engineering company in the world.
• Lead an energetic team of highly dynamic and talented individuals.
• Exposure to working with Fortune 500 companies and innovative market disruptors.
• Exposure to the latest technologies related to artificial intelligence, machine learning, data, and cloud.
• Work with happy, enthusiastic over-achievers in a culture built on transparency, diversity, integrity, learning, and growth.