Job Description

We currently have a career opportunity for an Technical Architect to join our team located in New York.

Job Overview:

We are seeking a distinguished technical leader to architect the next generation of our platform on Google Cloud (GCP). You will not just select services; you will design fault-tolerant, planetary-scale distributed systems that integrate state-of-the-art AI into the core of our engineering culture. You will bridge the gap between "Research" and "Production," applying SRE rigor to AI lifecycles.

The Technical Architect provides technology direction, ensures project implementation compliance, and utilizes technology research to innovate, integrate, and manage technology solutions. As a Technology Architect, you will significantly contribute to identifying best-fit architectural solutions for one or more projects; you will collaborate with some of the best talent in the industry to create and implement innovative high quality solutions, participate in Sales and various pursuits focused on our clients' business needs. This role is considered part of the Business Unit Leadership team and may mentor Junior Architects and /or development team members.

Responsibilities

• Architecting for "Google-Scale" & Reliability
  • Design highly resilient, cloud-native architectures rooted in SRE principles. You will define the strategy for GKE (Kubernetes) multi-cluster orchestration, service mesh (Istio/Anthos) connectivity, and global load balancing. You will enforce strict SLOs and Error Budgets at the architectural level to ensure high availability (99.99%+) while managing the trade-offs between consistency (Spanner) and availability.
    Productionizing Vertex AI & Large Models
• Orchestrate the end-to-end MLOps lifecycle leveraging the Vertex AI ecosystem.
  • You will architect pipelines that move models from notebooks to production seamlessly, managing feature stores (Vertex Feature Store) and model serving endpoints. You will lead the integration of Gemini/PaLM models, defining patterns for RAG (Retrieval-Augmented Generation) using Vector Search and BigQuery to deliver low-latency, context-aware AI.
• Implementing "Responsible AI" & Safety Guardrails
  • Operationalize AI Safety and Governance. You will architect rigorous evaluation frameworks to detect model drift, hallucination, and bias before deployment. You are responsible for implementing input/output guardrails and ensuring data sovereignty within GCP, treating model governance with the same rigor as IAM security policies.
• Performance Engineering & Compute Strategy
  • Define the Compute Strategy for AI workloads, optimizing the mix of TPUs, GPUs, and CPUs. You will own the architectural economics (FinOps), utilizing Spot instances, Autoscaling profiles, and custom machine types to maximize throughput-per-dollar. You will dive deep into profiling distributed training jobs to eliminate bottlenecks in data ingestion (Dataflow/PubSub).
• Cross-Functional "Design Doc" Culture:
  • Drive technical consensus through a rigorous Design Doc (RFC) culture. You will act as the technical authority between Product and Engineering, translating ambiguous business requirements into concrete, engineering-ready architectural blueprints. You will lead "Architecture Review Boards" to ensure every microservice adheres to the broader ecosystem vision.
• Codifying the "Paved Path" (Platform Engineering)
  • Build the "Golden Path" for developers. Instead of manual policing, you will architect Infrastructure-as-Code (Terraform) modules and policy-as-code (OPA/kpt) that make doing the right thing the easiest thing. You will define the standard libraries and distinct abstractions that allow feature teams to ship code without reinventing the wheel.

Qualifications

• Bachelor's degree in Computer Science, Computational Linguistics, Mathematics, or equivalent practical engineering experience.
• 5+ years of software engineering experience with fluency in one or more languages (Python, Go, Java, or C++). You must be capable of code reviews and architectural prototyping.
• 2+ years of hands-on experience designing and deploying LLM-backed applications in high-throughput production environments. Experience goes beyond API calls to include fine-tuning, quantization, and context-window optimization.
• Demonstrated mastery of modern agentic frameworks (LangChain, LlamaIndex, AutoGPT) with a portfolio of complex, multi-turn conversational agents that execute deterministic actions (tool use/function calling).
• Solid understanding of distributed systems design, microservices, and API design principles (gRPC/REST). You understand CAP theorem, latency vs. throughput trade-offs, and how to design for failure.
• Demonstrated ability to leverage AI tools to enhance productivity, streamline workflows, and support data-informed task execution.
• Familiarity with AI-enhanced platforms is a plus.
• A solid understanding of AI capabilities and limitations including ethical considerations is expected. The highlighted requirements cannot be changed per Yusuf

Preferred Qualifications

• Advanced AI Depth: Master's degree or PhD in Artificial Intelligence, NLP, or Machine Learning.
• Google Cloud Ecosystem: Deep familiarity with the GCP AI stack: Vertex AI, BigQuery, Dataflow, and Kubernetes Engine (GKE). Certification (Professional Cloud Architect or ML Engineer) is a strong plus.
• MLOps at Scale: Experience architecting end-to-end MLOps pipelines (TFX, Kubeflow, MLflow) that automate training, evaluation, deployment, and monitoring of large-scale models.
• Cognitive Architecture: Deep knowledge of prompt evaluation methodologies (ROUGE, BLEU, Human-in-the-loop) and experience implementing RAG (Retrieval-Augmented Generation) patterns using vector databases (Pinecone, Milvus, Weaviate).
• Strategic Leadership: Proven ability to lead "whiteboard-to-production" journeys. Experience navigating ambiguity, influencing C-level stakeholders, and driving technical consensus across cross-functional engineering and product teams.

About the team:

Our Cloud team helps the world's leading brands modernize infrastructure, applications, and integrations to thrive in today's digital economy. We deliver cloud-native development, app modernization, API management, and DevOps solutions, enabling clients to maximize cloud investments, streamline processes, and drive innovation across the enterprise. Join a team that's harnessing the power of AI to build intelligent, self-optimizing cloud environments-leveraging predictive analytics, generative tools, and automation to create smarter, more resilient digital ecosystems.

Applications will be accepted until the position is filled or the posting is removed.

The salary range for this position takes into consideration a variety of factors, including but not limited to skill sets, level of experience, applicable office location, training, licensure and certifications, and other business and organizational needs. The new hire salary range displays the minimum and maximum salary targets for this position across all US locations, and the range has not been adjusted for any specific state differentials. It is not typical for a candidate to be hired at or near the top of the range for their role, and compensation decisions are dependent on the unique facts and circumstances regarding each candidate. A reasonable estimate of the current salary range for this position is $80,000.00 to $160,000.00. Please note that the salary range posted reflects the base salary only and does not include benefits or any potential variable compensation programs. Information regarding the benefits available for this position are in our benefits overview.

Disclaimer: The above statements are not intended to be a complete statement of job content, rather to act as a guide to the essential functions performed by the employee assigned to this classification. Management retains the discretion to add or change the duties of the position at any time.

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