Job Overview

Determines problems within specific systems and provides solutions, including designing new systems, platforms or test applications.

Job Requirements

• • Develop applications for automation, backend services, data processing, and user-facing solutions.
  • Build and integrate API-driven services across engineering tools, enterprise systems, and databases.
  • Design and implement data flows that connect Engineering evidence.
  • Develop modular and reusable software components.
  • Integrate AI/ML capabilities into engineering and business applications.
  • Support deployment of PoCs and manage the roll-out on-premises or at the cloud.
  • Work with engineering stakeholders on solutions which deliver clean data.
  • Apply structured development practices including code reviews, testing, documentation, release management, and version control.
  • Evaluate technology choices based on scalability, maintainability, reuse, and business value.
  • Maintain a feature-first approach, prioritizing usable functionality and simplicity while avoiding unnecessary architectural complexity and tool proliferation.
  • Take each task with full scope of responsibility and a hands-on mindset.

What your background should look like

• 3–6  years of relevant professional experience in software development, AI/ML engineering, systems integration, engineering software, automation, or a related field.
• Strong proficiency in Python and React for the frontend.
• Practical experience with REST APIs, API-based integration, and service-oriented architectures (including MCP).
• Strong understanding of SQL, relational data modeling, and database design.
• Working knowledge of NoSQL databases and their appropriate use cases.
• Good understanding of modular software architecture, reusable components, and separation of concerns.
• Familiarity with cloud computing architectures (AWS and Azure).
• Basic understanding of edge computing and distributed application architectures.
• Working knowledge of machine learning and deep learning concepts, including model training, inference, evaluation, and deployment.
• Familiarity with generative AI, LLM-based applications, AI agents, or AI-enabled workflow automation is desirable.
• Working knowledge of Git and collaborative software development practices.
• Understanding of software quality practices including testing, debugging, documentation, and code review.
• Exposure to engineering, manufacturing, industrial automation, IoT, machine data, or process data.
• Basic familiarity with CAD/CAE, simulation, digital twins, or model-based engineering.
• Strong analytical and problem-solving capability with the ability to convert loosely defined requirements into implementable solutions.
• Understanding of a scalable and adoptable UX design.

Competencies