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

Obin AI; New York (NY); On-site; USD 180,000 - 280,000; Full time

Job summary

Obin AI seeks a Principal Solution Architect to set the architectural strategy for enterprise multi-cloud platforms and next‑generation AI initiatives in financial services.

You will translate complex distributed systems into robust production software, land agentic capabilities across top financial institutions, and lead cross‑functional teams from research to executive stakeholders.

Qualifications

• Architect multi-agent orchestration frameworks and agent loops.
• Implement RAG systems and agent harnesses across distributed AI workflows.
• Lead enterprise data pipelines using Spark on Kubernetes and cloud data warehousing.

Responsibilities

• Design autonomous, multi-agent ecosystems and A2A protocols.
• Architect GenAI apps with RAG, voice agents and vector routing.
• Build scalable multi-cloud Kubernetes platforms across GCP, AWS, and Azure.
• Ingest and shard large data streams; run Spark on Kubernetes.
• Advocate technical excellence with reusable blueprints and docs.

Skills

Agentic AI

Generative Engineering

Multi-Cloud

Kubernetes

Data Pipelines

CTO Experience

Open-source Advocacy

Tools

Terraform

Kubernetes

Spark

Airflow

Job description

The Role

We are seeking an industry-leading Principal Solution Architect to design the architectural strategy for enterprise multi‑cloud platforms and next‑generation AI initiatives at financial firms.

In this elite role, you will bring Obin’s agentic capabilities and land them within the complex technical environments of leading financial institutions. You will design distributed multi‑cloud architectures (GCP, AWS, Azure) capable of running high‑throughput data pipelines, optimizing latency of LLM calls, and orchestrating state‑of‑the‑art Agentic AI workflows (collaborative multi‑agent protocols, generative storytelling, and autonomous task delegation). You are a technical visionary who translates non‑deterministic agentic capabilities and complex distributed computing into robust production software.

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.