Time since we first saw it
Day 128
Up longer than most. Similar jobs usually come down between day 4 and day 77.
From 51 similar postings that came down. Coming down is not always a hire.
Principal Solution Architect (Multi-Cloud, Data & Agentic AI)
- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: FULL_TIME
From the employer’s posting
What you’ll work on
Full postingDesign multi-agent ecosystems with agent-to-agent orchestration and task delegation
Architect RAG, voice agent and streaming agent infrastructure
Build and scale containerized services across GCP, AWS and Azure using Terraform and Kubernetes
Architect decoupled data pipelines running Spark on Kubernetes with BigQuery or Snowflake
Publish reusable blueprints and reference architectures and align research, engineering and executives
From the employer’s posting
Design and build autonomous, multi-agent ecosystems.
Architect infrastructure to support sophisticated Generative AI applications, including Retrieval-Augmented Generation (RAG), voice agents, streaming agent output, and vector database routing.
Build, secure, and scale containerized applications and microservices topologies natively across Google Cloud, AWS, and Azure.
Architect decoupled data ecosystem frameworks to ingest and shard massive streams of structured and unstructured data.
Champion technical excellence by publishing reusable blueprints, reference code architectures, and technical documentation.
Posting record
Report an errorOpen 4 monthsWe first saw this posting 128 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jul 8, 2026128 days ago
- Recorded sightings
- 60
- Last seen by us
- Nov 13, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Obin-Ai is hiring
2 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- AWS
- Azure
- BigQuery
- GCP
- Kubernetes
- Snowflake
- Spark
- Terraform
- Airflow
Where the posting names them
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
- 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).
- 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.
- 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.
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).
Your next step
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Demand for AI Engineer roles
New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
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About the role
We file this job under AI Engineer, by the work the posting describes.
AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.
Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.
An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
NYC-Metro
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.