Invisible Technologies Inc.

Principal Software Engineer

Invisible Technologies Inc.

full-time

Posted on:

Origin:  • 🇬🇧 United Kingdom

Visit company website
✨ AI Apply
Apply

Job Level

Lead

Tech Stack

AWSAzureCloudGoogle Cloud PlatformKubernetesNoSQLPythonSQL

About the role

  • Contribute during a phase of rapid growth, focusing on scaling models and improving platform performance as part of the Forward Deployed Engineering team.
  • Build backend systems, support client-facing deployments, and enable smoother workflows for machine learning solutions.
  • Develop and maintain AI/ML systems: build robust, scalable backend systems that support machine learning operations and data processing pipelines.
  • Oversee and optimize cloud infrastructure to ensure efficient deployment and operation of ML models.
  • Independently explore and address complex problem spaces to improve system capabilities and performance.
  • Work closely with ML engineers and data scientists to integrate advanced ML technologies across platforms.
  • Collaborate directly with clients, embedded with client teams to support use case discovery, product development, and AI deployment.
  • Actively participate in research and development of new tools to enhance AI capabilities and workflows.
  • Combine hands-on model development with backend engineering and infrastructure work to enable rapid iteration and production-ready deployments.

Requirements

  • 5+ years of software engineering experience, with a strong focus on ML engineering and deploying machine learning models in production.
  • Extensive experience in full-stack development, particularly in backend environments that support AI/ML workloads.
  • Prior experience working directly with clients in use case discovery, product development, and leading client engagements.
  • Strong proficiency in Python, with deep expertise in LLMs, AI Agents, and ML model development.
  • Experience designing and deploying scalable ML systems, such as retrieval-augmented generation (RAG) pipelines and production-grade AI applications.
  • Extensive experience with cloud platforms (AWS, GCP, Azure) and operational best practices for ML workloads.
  • Familiarity with Kubernetes and other container management tools.
  • Ability to write well-structured, organized code and automated unit/E2E tests.
  • Comfortable with polyglot persistence models (SQL vs. NoSQL).
  • ML Operations: Experience with MLOps frameworks and best practices; familiarity with DevOps principles as applied to machine learning models, including model versioning, monitoring, and lifecycle management.
  • Ability to operate independently in unstructured environments and proactively investigate challenges.
  • Excellent communication skills and experience collaborating with data scientists, researchers, and software engineers.