AI Foundation Architect Lead

Univers; New Haven (CT); On-site; USD 180,000 - 260,000; Full time

Job summary

Univers seeks a forward-thinking AI Platform Foundation Architect to lead evolution of our core platform into an AI-native, scalable ecosystem. You will define architecture across IaaS, Middleware, Big Data, and AI Infra, and translate strategy into tangible platforms.

You will drive automated deployments, monitoring, and fault-tolerant design, embed AI gateways and lightweight models, and collaborate with product, engineering, and operations teams to accelerate innovation.

Qualifications

• AI Infra & Model Serving: Hands-on with AI Gateways, token optimization, hosting small/open-source models.
• AIOps & Smart O&M: Use LLMs/AI Agents for log analysis, RCA, and automated checks.
• Middleware & Data Engines: Deep knowledge of Kafka, Redis, and time-series platforms.
• IaaS & Cloud-Native: Experience with public/hybrid clouds, Kubernetes, and IaC tools.
• Observability & Resource Optimization: Proficient with Prometheus, Grafana, and ELK.
• Distributed Systems: Proven ability to design scalable, secure platforms.
• Communication: Aligns executives, engineers, product teams, and customers.
• Education: CS degree or equivalent.

Responsibilities

• Oversee evaluation, architecture standards, and operating frameworks for enterprise middleware.
• Architect and optimize high-throughput Big Data platforms, TSDBs, and log/metric storage engines.
• Provision scalable cloud-native and hybrid IaaS solutions using IaC.
• Design automated horizontal and vertical resource scaling to balance performance and cost.
• Establish full-stack telemetry and monitoring for critical hardware and software metrics.
• Architect multi-region, fault-tolerant systems to guarantee platform availability.
• Design unified backup workflows, automated snapshots, and rapid DR execution.
• Integrate AI agents and LLM/SLM capabilities into CI/CD pipelines and RCA tooling.
• Architect and deploy centralized AI Gateways to manage token usage, caching, rate-limiting, and cost allocation across LLMs/SLMs.
• Design low-latency hosting for small/edge models and open-source models.
• Build a unified AI Infra layer providing model serving, GPU/NPU orchestration, and MCP routing.

Skills

AI Infra & Model Serving

AIOps & Smart O&M

Middleware & Data Engines

IaaS & Cloud-Native

Observability & Resource Optimization

Distributed Systems

Communication

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

Kafka

RabbitMQ

Redis

IoTDB

HBase

InfluxDB

Flink

Terraform

Ansible

Kubernetes

Prometheus

Grafana

Loki/ELK

Job description

Univers combines global operating scale, deep industrial intelligence, and recognition from the institutions shaping energy, technology, and sustainability. The platforms built for the last decade were designed to monitor.

Univers was built to act — not just report.

Univers operates at global scale:

• 1,070 GW+ Energy assets under AI management
• 450M+ Connected sensors and devices
• 800+ Enterprise customers
• Leader Gartner Magic Quadrant Leader

Home - Univers

Job Summary

We are seeking a forward-thinking and hands-on AI Platform Foundation Architect to spearhead the evolution of our core platform foundation into an AI-native, highly scalable tech ecosystem. In this role, you will be responsible for defining and executing the architecture across Infrastructure as a Service (IaaS), Middleware, Big Data platforms, and AI Infrastructure (AI Infra).

Beyond core infrastructure management (HA, auto-scaling, backup, and performance), you will act as a key transformation catalyst—building enterprise AI Gateway capabilities for LLM/SLM token and cost optimization, hosting small models, and embedding AI capabilities into automated operations, deployment, and root-cause troubleshooting. You will bridge traditional infrastructure engineering with modern AI agentic operations to empower all upper-level platform and product teams.

Key Responsibilities

• Oversee the evaluation, selection, architecture standards, and operational frameworks for enterprise middleware (e.g., distributed messaging, caches, databases, API gateways).
• Architect and optimize high-throughput Big Data platforms, time-series databases (TSDB), and log/metric storage engines handling high-concurrency real-time telemetry.
• Provision scalable cloud-native and hybrid IaaS solutions (compute, networking, storage, K8s) using Infrastructure as Code (IaC) principles.
• Design automated horizontal and vertical resource scaling (expansion/shrinkage) to ensure performance under peak traffic while minimizing infrastructure spend.
• Establish full-stack telemetry and monitoring to track critical hardware and software metrics (IO, CPU, RAM, Storage, Network).
• Architect multi-region, resilient fault-tolerant systems to guarantee non-disruptive platform availability.
• Design unified backup workflows, automated snapshots, and rapid DR execution to safeguard core data assets.
• Integrate AI agents and LLM/SLM capabilities directly into CI/CD pipelines, automated deployments, anomaly detection, real-time log analysis, and root-cause analysis (RCA) to accelerate incident resolution.
• Architect and deploy centralized AI Gateways (e.g., LiteLLM, Portkey, Kong AI) to manage, route, and optimize token usage, semantic prompt caching, rate-limiting, and cost allocation across foundational LLMs/SLMs.
• Design low-latency, scalable hosting and inference infrastructure for small/edge models (SLMs) and open-source models to support lightweight, specialized internal tasks.
• Build a unified AI Infra layer providing model serving, GPU/NPU resource orchestration, and model context protocol (MCP) routing for upper-level teams.

Basic Qualifications

• AI Infra & Model Serving: Hands-on experience with AI Gateways, token optimization strategies (prompt caching, model routing), and hosting small language models (SLMs) or open-source models using tools like vLLM, Ollama, or Triton.
• AIOps & Smart O&M: Familiarity with leveraging LLMs/AI Agents for log analysis, automated deployment checks, root cause analysis (RCA), or AI-driven alerting.
• Middleware & Data Engines: Deep architecture expertise in messaging queues (Kafka, RabbitMQ), caches (Redis), and modern data/time-series platforms (IoTDB, HBase,InfluxDB, Flink Lakehouse architectures).
• IaaS & Cloud-Native: Strong experience in public/hybrid clouds (AWS, GCP, Azure), Kubernetes orchestration, and Infrastructure as Code (Terraform, Ansible).
• Observability & Resource Optimization: Expert-level knowledge of observability tools (Prometheus, Grafana, Loki/ELK) for monitoring CPU, IO, Storage, and system bottlenecks.
• Solid understanding of distributed systems; hands-on experience in designing and developing platforms for traffic risk control, transaction fraud detection, or account security is highly preferred.
• Ability to thrive in a fast-paced, dynamic environment and drive projects forward with efficiency.
• Experience integrating heterogeneous enterprise systems and working with both IT data and OT/IoT data from industrial or physical environments.
• Excellent communication, with the ability to align executives, engineers, product teams, field teams and customers around difficult technical decisions.
• Bachelor’s or Master’s degree in Computer Science, Software Engineering or equivalent practical experience.

Preferred

• Experience with agentic AI, RAG, model and agent governance, tool permissioning, human-in-the-loop workflows or governed write-back.
• Experience establishing developer platforms, APIs, SDKs, connector ecosystems or open-source and partner programs.
• Experience leading globally distributed teams and delivering platforms across multiple regulatory and deployment environments.