Senior AI Infrastructure Engineer

About Us

"AI operating system"

enterprise-grade security

We are looking for a foundational engineer to join our small, high-impact team. If you are passionate about building secure, scalable, and self-evolving AI systems from the ground up, this is your opportunity to define the future of investment technology.

The Role

own the deployment, optimization, and operational excellence

This is a critical, high-ownership role. You will be responsible for building the production-grade MLOps pipelines, secure back-end services, and AIOps frameworks. You will ensure our platform meets the "demanding investment environments" of our clients by guaranteeing scalability, reliability, and our promise of data sovereignty.

Core Responsibilities

• Architect Enterprise AI Systems:

  Design and build the scalable, secure back-end microservices (Python/FastAPI) that serve our "AI analyst," agentic workflows, and custom-tuned LLMs.

• Lead MLOps Implementation:

  Build and own the end-to-end MLOps lifecycle. This includes automated CI/CD for model deployment, model versioning, and creating pipelines for

  fine-tuning models to each client's unique investment thesis

  .

• Cloud Deployment & Orchestration:

  Deploy, scale, and manage our AI services using

  Docker

  and

  Kubernetes (AKS, EKS)

  . You will be the primary owner of our

  Infrastructure as Code (Terraform)

  , responsible for provisioning all cloud resources.

• Secure Multi-Cloud Architecture:

  Implement and manage our

  data sovereignty

  strategy across

  AWS and Azure

  , ensuring sensitive client data remains secure, segregated, and compliant.

• Performance & Cost Optimization:

  Relentlessly profile and optimize our AI models (e.g., quantization, inference speed) and infrastructure for latency, throughput, and cost. Implement serverless (Lambda/Azure Functions) and auto-scaling logic.

• Develop AIOps Solutions:

  Build and manage a comprehensive monitoring, logging, and alerting framework (

  CloudWatch, Azure Monitor, Prometheus

  ).

Key Qualifications & Skills

We are looking for a seasoned, hands-on expert who thrives in a fast-paced startup environment and has a track record of running large-scale AI systems in production.

Required Experience:

• 7+ years

  of professional software engineering experience, with at least

  3+ years in a senior or lead role

  focusing on cloud infrastructure, DevOps, or MLOps.
• Demonstrable, hands-on expertise in

  architecting and deploying production AI/ML workloads on both AWS and Azure

  .
• Proven experience building and maintaining high-availability, secure back-end systems.
• The ability to work independently, make critical technical decisions, and build systems from the ground up.

Technical Skillset:

• Back-End:

  Deep expertise in

  Python

  and frameworks like

  FastAPI

  , Flask, or Django.

• AI / ML:

• Strong practical knowledge of

  PyTorch

  or

  TensorFlow

  .
• Experience deploying

  Generative AI (LLMs)

  and RAG systems in production.
• Proficiency with MLOps frameworks (e.g.,

  MLflow

  , Kubeflow).

• Databases:

  Expertise with both SQL and NoSQL databases. 

• MLOps, DevOps & AIOps:

• Orchestration/:

  Expert-level

  Docker

  &

  Kubernetes (AKS, EKS)

  . Deep proficiency with

  Terraform

  (preferred) or CloudFormation/Bicep.

• CI/CD:

  Mastery of

  Azure DevOps (Pipelines)

  or

  AWS CodePipeline

  / GitHub Actions.

• Monitoring (AIOps):

  Hands-on experience with

  AWS CloudWatch

  ,

  Azure Monitor

  , Prometheus, and Grafana.

  Logging:

  Experience with

  Amazon OpenSearch Service

  or

  Azure Log Analytics