GCP Practice Architect II-AI/ML

TEKsystems•Baltimore, MD

5d•$156,000 - $234,000•Remote

About The Position

Think of TEKsystems Global Services (TGS) as the growth solution for enterprises today. We unleash growth through technology, strategy, design, execution and operations with a customer-first mindset for bold business leaders. We deliver cloud, data and customer experience solutions. Our partnerships with leading cloud, design and business intelligence platforms fuel our expertise. We value deep relationships, dedication to serving others and inclusion. We drive positive outcomes for our people and our business, and we stay true to our commitments and act in harmony with our words. We exist to create significant opportunity for people to achieve fulfillment through career success. Ready to join us? Here’s what the opportunity supported through our TGS Talent Acquisition Team requires: Architecture & Delivery Excellence: · Lead the technical design and architecture of complex, end-to-end AI/ML solutions on Google Cloud Platform, leveraging a comprehensive suite of services including Vertex AI (Pipelines, Training, Prediction, Feature Store), BigQuery ML, AutoML, Generative AI capabilities (e.g., leveraging Gemini, RAG patterns), Document AI, and Contact Center AI. Incorporate modern GenAI orchestration frameworks (e.g., LangChain, LlamaIndex) and vector databases (e.g., Vertex AI Vector Search, AlloyDB with pgvector). · Develop and implement robust, scalable, and production-grade AI/ML systems, ensuring they meet stringent performance, security, and reliability requirements for enterprise clients. Ensure infrastructure is deployed securely using Infrastructure as Code (IaC) standards like Terraform, enforcing GCP security best practices (VPC Service Controls, IAM, CMEK). · Architect and implement comprehensive MLOps and LLMOps frameworks on GCP for efficient model development, deployment, monitoring, and lifecycle management, including CI/CD pipelines, model versioning, and automated retraining strategies. · Design and oversee the implementation of data engineering pipelines on GCP for AI/ML use cases, covering data ingestion, preprocessing, feature engineering, and data governance using services like Google Cloud Storage, BigQuery, Dataflow, and Pub/Sub. · Embed Responsible AI and AI Governance into architectures, utilizing tools like Vertex Explainable AI and implementing data privacy guardrails to mitigate risks of model bias, toxicity, and hallucinations. Project Leadership & Delivery Management: · Lead the technical delivery of significant GCP AI/ML projects, ensuring adherence to architectural best practices, project scope, timelines, and budget constraints. · Provide technical leadership and guidance to cross-functional project teams, including AI/ML engineers, data scientists, and data engineers, fostering a collaborative and high-quality delivery environment. · Manage technical risks and issues throughout the project lifecycle, facilitating timely resolution and communicating effectively with stakeholders. · Ensure financial and contractual responsibility for the profitability of assigned AI/ML engagements. Apply Cloud FinOps principles to AI workloads, right-sizing compute (GPUs/TPUs) and optimizing token usage to manage enterprise cloud spend. · Conduct deep-dive "hands-on" education/training sessions to transfer knowledge to customers considering, or already using GCP Client Engagement & Technical Advisory · Collaborate closely with client stakeholders to understand their business objectives, technical requirements, and challenges, translating them into effective GCP AI/ML solution architectures. Lead Value Engineering efforts to help clients articulate the ROI of AI/ML initiatives, ensuring clear KPIs are established to move projects successfully from Proof of Concept (PoC) into enterprise production. · Present and articulate complex technical solutions, architectural designs, and project progress to both technical and non-technical client audiences, building trust and ensuring alignment. · Act as a key technical point of contact for clients during project execution, addressing concerns and managing expectations. · Support pre-sales activities by contributing to solution design, proposal development, technical presentations, and proof-of-concept (PoC) demonstrations for GCP AI/ML opportunities. Plan, facilitate, and lead strategic workshops, hackathons, and ideation sessions for customers within the Google ecosystem, leveraging partner-led discovery frameworks to drive GenAI and ML adoption. Practice Development & Mentorship: · Contribute significantly to the development and refinement of TEKsystems' GCP AI/ML practice by creating reusable intellectual property (IP), best practices, reference architectures, and delivery methodologies. · Mentor and guide junior architects and AI/ML engineers, fostering their technical and professional growth within the practice. Team sizes for mentorship can range from 3 to over 10 members. · Stay current with the latest advancements in GCP AI/ML services, open-source frameworks, and industry trends, sharing knowledge and driving innovation within the team. · Participate in architectural discussions to build confidence and ensure customer success when building new and migrating existing applications, software, and services on the GCP platform.

Requirements

Bachelor’s/master’s degree in computer science, Engineering, Data Science, Artificial Intelligence, Mathematics, or a related technical field, or equivalent, relevant experience.
A minimum of 12+ years of progressive experience in Information Technology, with a strong emphasis on AI/ML, data analytics, and cloud computing.
At least 5-7+ years of direct, hands-on experience in designing, developing, and implementing enterprise-grade AI/ML solutions, with substantial project experience on Google Cloud Platform. This includes a proven ability to architect for scalability, reliability, and performance.
A minimum of 2-3+ years of experience in a technical leadership role, guiding project teams or significant workstreams in the delivery of AI/ML solutions.
Demonstrated experience with the complete lifecycle of AI/ML projects on GCP, from initial requirements gathering and PoC development through to production deployment, monitoring, and optimization.
Proven experience in architecting and implementing MLOps systems in enterprise environments, including building, scaling, and optimizing machine learning systems on GCP.
Strong client-facing experience, including presenting technical solutions, participating in workshops, and effectively communicating with diverse stakeholders. Experience in a software/technology customer-facing role for at least 5 years is highly relevant.
Experience in developing data and AI solutions utilizing Google Cloud Data services, and architecting System Design for Data & AI Modernization efforts.
At least 1 year of experience in Generative AI using Hyperscalers, employing techniques such as RAG, prompt engineering, and fine-tuning custom models from a marketplace. This reflects the growing importance of Generative AI skills at this level.
Architecture
Cloud
Google Cloud
Design
Solution architecture
Development
Enterprise architecture
Professional services
Artificial intelligence
Machine learning
Agile
AWS
Azure
Java
Automation
DevOps
Data
Project management
Communication and leadership skills
Terraform/IaC
LangChain/GenAI Orchestration
FinOps
Responsible AI
Value Engineering

Nice To Haves

Active Google Cloud Professional Certification is highly preferred (Professional Machine Learning Engineer and/or Professional Cloud Architect), reflecting our commitment to the Google Cloud Partner Advantage ecosystem.

Responsibilities

Lead the technical design and architecture of complex, end-to-end AI/ML solutions on Google Cloud Platform, leveraging a comprehensive suite of services including Vertex AI (Pipelines, Training, Prediction, Feature Store), BigQuery ML, AutoML, Generative AI capabilities (e.g., leveraging Gemini, RAG patterns), Document AI, and Contact Center AI. Incorporate modern GenAI orchestration frameworks (e.g., LangChain, LlamaIndex) and vector databases (e.g., Vertex AI Vector Search, AlloyDB with pgvector).
Develop and implement robust, scalable, and production-grade AI/ML systems, ensuring they meet stringent performance, security, and reliability requirements for enterprise clients. Ensure infrastructure is deployed securely using Infrastructure as Code (IaC) standards like Terraform, enforcing GCP security best practices (VPC Service Controls, IAM, CMEK).
Architect and implement comprehensive MLOps and LLMOps frameworks on GCP for efficient model development, deployment, monitoring, and lifecycle management, including CI/CD pipelines, model versioning, and automated retraining strategies.
Design and oversee the implementation of data engineering pipelines on GCP for AI/ML use cases, covering data ingestion, preprocessing, feature engineering, and data governance using services like Google Cloud Storage, BigQuery, Dataflow, and Pub/Sub.
Embed Responsible AI and AI Governance into architectures, utilizing tools like Vertex Explainable AI and implementing data privacy guardrails to mitigate risks of model bias, toxicity, and hallucinations.
Lead the technical delivery of significant GCP AI/ML projects, ensuring adherence to architectural best practices, project scope, timelines, and budget constraints.
Provide technical leadership and guidance to cross-functional project teams, including AI/ML engineers, data scientists, and data engineers, fostering a collaborative and high-quality delivery environment.
Manage technical risks and issues throughout the project lifecycle, facilitating timely resolution and communicating effectively with stakeholders.
Ensure financial and contractual responsibility for the profitability of assigned AI/ML engagements. Apply Cloud FinOps principles to AI workloads, right-sizing compute (GPUs/TPUs) and optimizing token usage to manage enterprise cloud spend.
Conduct deep-dive "hands-on" education/training sessions to transfer knowledge to customers considering, or already using GCP
Collaborate closely with client stakeholders to understand their business objectives, technical requirements, and challenges, translating them into effective GCP AI/ML solution architectures. Lead Value Engineering efforts to help clients articulate the ROI of AI/ML initiatives, ensuring clear KPIs are established to move projects successfully from Proof of Concept (PoC) into enterprise production.
Present and articulate complex technical solutions, architectural designs, and project progress to both technical and non-technical client audiences, building trust and ensuring alignment.
Act as a key technical point of contact for clients during project execution, addressing concerns and managing expectations.
Support pre-sales activities by contributing to solution design, proposal development, technical presentations, and proof-of-concept (PoC) demonstrations for GCP AI/ML opportunities. Plan, facilitate, and lead strategic workshops, hackathons, and ideation sessions for customers within the Google ecosystem, leveraging partner-led discovery frameworks to drive GenAI and ML adoption.
Contribute significantly to the development and refinement of TEKsystems' GCP AI/ML practice by creating reusable intellectual property (IP), best practices, reference architectures, and delivery methodologies.
Mentor and guide junior architects and AI/ML engineers, fostering their technical and professional growth within the practice. Team sizes for mentorship can range from 3 to over 10 members.
Stay current with the latest advancements in GCP AI/ML services, open-source frameworks, and industry trends, sharing knowledge and driving innovation within the team.
Participate in architectural discussions to build confidence and ensure customer success when building new and migrating existing applications, software, and services on the GCP platform.