Principal Solution Architect (Multi-Cloud, Data & Agentic AI)

Location: NYC-Metro

Department: Engineering/Product

Location Type: HYBRID

Employment Type: FULL_TIME

The Role

Key Responsibilities

• Agentic AI & Collaborative Frameworks: Design and build autonomous, multi-agent ecosystems. Implement cutting-edge agent-to-agent (A2A) orchestration protocols, enabling secure task delegation, runtime state handoffs, and collaborative workflows without context pollution.
• Generative AI: Architect infrastructure to support sophisticated Generative AI applications, including Retrieval-Augmented Generation (RAG), voice agents, streaming agent output, and vector database routing.
• Multi-Cloud Infrastructure & Kubernetes Mastery: Build, secure, and scale containerized applications and microservices topologies natively across Google Cloud, AWS, and Azure. Establish highly declarative environments using Infrastructure as Code (Terraform) and advanced Kubernetes orchestration (GKE, EKS).
• Distributed Enterprise Data Pipelines: Architect decoupled data ecosystem frameworks to ingest and shard massive streams of structured and unstructured data. Run distributed processing engines like Apache Spark on Kubernetes alongside cloud-native data warehouses (BigQuery, Snowflake).
• Technical Advocacy & Inner-Sourcing: Champion technical excellence by publishing reusable blueprints, reference code architectures, and technical documentation. Act as a cross-functional leader aligning research teams, core engineering, and executive stakeholders.

Required Skills & Experience

1. Agentic AI & Generative Engineering

• Advanced AI Paradigms: Proven experience architecting multi-agent orchestration frameworks, prompt-routing middleware, and self-correcting agent loops.
• AI architectures: Experience with implementing RAG systems, agent harnesses, deep research agents and other agentic workflows

2. Multi-Cloud & Enterprise Data Foundations

• Distributed Systems: Expert-level knowledge running heavy analytical workloads (e.g., Spark, Airflow) natively inside containerized ecosystems (Kubernetes).
• Cloud Topologies: Comprehensive, production-level engineering experience across at least two hyperscalers (GCP or AWS or Azure).
• Data Mechanics: Strong grasp of modern data storage paradigms, including columnar storage, vector indexing, and automated data lake governance.

3. Leadership & Vision

• Experience acting as a CTO, Principal Architect, or Strategic Lead driving large-scale digital transformations or public-sector AI cloud implementations.
• Strong background in developer advocacy, open-source contributions, or technical publication.