Software Engineer, Agentic Ai

Invisible Technologies  ยท  via Remote Communications Jobs
๐Ÿ“ Brazil โฐ Full Time

About the Role

Invisible Technologies is hiring a Software Engineer (Agentic AI) to build and deploy production-grade ML systems and backend infrastructure. The role blends full-stack/backend engineering, MLOps, and client-facing work to scale agentic AI, LLM-based solutions, and RAG pipelines in production environments.

Directly aligned โ€” builds agentic AI, LLMs, AI Agents and RAG pipelines and focuses on rapid deployment and MLOps workflows.

Location: Brazil

Salary: BRL 249,000 - 409,000/year

Type: {Full-Time}

Posted: Yesterday

Category: AI/ML & Data

Vibe Markers: ๐Ÿ’ป Open Source, ๐Ÿ”ฅ Hot

Experience Level: Mid

Job Description

Role

Invisible is seeking a Software Engineer (Agentic AI) to join its AI/ML team and Forward Deployed Engineering organization. The role combines hands-on ML model development with backend engineering and infrastructure work, embedding with clients to discover use cases and deploy production ML solutions.

Key Responsibilities

  • Build and maintain robust, scalable backend systems and data processing pipelines to support ML workloads.
  • Oversee and optimize cloud infrastructure for efficient deployment and operation of ML models.
  • Support R&D initiatives and develop new tools to enhance AI capabilities and workflows.
  • Collaborate closely with ML engineers, data scientists, and client teams to integrate advanced ML technologies.
  • Work embedded with clients to conduct use case discovery, lead client engagements, and support product development and deployment.
  • Independently investigate and solve complex technical problems and enable rapid iteration and deployment.

Requirements

  • 2+ years of software engineering experience with a strong focus on ML engineering and deploying machine learning models to production.
  • Extensive full-stack development experience, with emphasis on backend systems that support AI/ML workloads.
  • Strong proficiency in Python and demonstrated experience with 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.
  • Experience with cloud platforms (AWS, GCP, Azure) and operational best practices for ML workloads.
  • Familiarity with Kubernetes and container management tools.
  • Experience with polyglot persistence models (SQL and NoSQL) and writing well-structured code with automated unit/E2E tests.
  • Practical knowledge of MLOps frameworks, model versioning, monitoring, and lifecycle management.
  • Strong problem-solving skills, proactive independence in unstructured environments, and excellent cross-functional communication for client-facing work.

Compensation & Benefits

  • Annual salary range listed: R$ 249.000 - R$ 409.000 BRL (tier depends on location and level).
  • Bonuses and equity are included in all full-time offers.
  • Compensation is adjusted for geographic pay tiers and local market conditions.

Work Location & Style

  • Invisible operates as a hybrid organization with offices and team members located globally; many roles involve in-office collaboration and may be tied to specific locations. Specific hybrid or location expectations will be communicated during recruiting.

Note

Boilerplate legal and accessibility statements were omitted. Hiring process may include AI-assisted steps and reasonable accommodation options are available.

Tech Stack

Python, LLMs, AI Agents, Retrieval-Augmented Generation (RAG), AWS, GCP, Azure, Kubernetes, SQL, NoSQL, MLOps frameworks

Skills

Full-stack Development, Backend Engineering, Machine Learning Engineering, MLOps, Cloud Infrastructure Management, Kubernetes / Container Orchestration, Scalable Systems Design, Polyglot Persistence (SQL and NoSQL), Automated Testing (Unit and E2E), Client-facing Collaboration, Cross-functional Communication, Problem Solving, Research & Development

Benefits

  • Bonuses
  • Equity
  • Competitive pay (regionally adjusted)
  • Hybrid work / in-office collaboration
  • Accessibility accommodations
  • Equal opportunity employer