Digital Engineering - SW & AI

Key Responsibilities
Develop Python-based applications for automation, backend services, data processing, and user-facing solutions.
Build and integrate API-driven services across engineering tools, enterprise systems, databases, and AI platforms.
Design and implement data flows that connect laboratory, manufacturing, machine, process, simulation, and application data.
Develop modular and reusable software components that can be shared across multiple workflows and applications.
Integrate AI/ML capabilities into engineering and business applications.
Support deployment of applications across cloud, on-premise, and edge environments where required.
Translate engineering and business requirements into practical software features and technical solutions.
Work with engineering, manufacturing, laboratory, automation, and simulation teams to understand workflows, system interfaces, and integration needs.
Enable structured use of physical test results, laboratory reports, and engineering data within digital workflows.
Support integration with CAD/CAE, simulation, digital twin, and model-based engineering environments.
Apply structured development practices including code reviews, testing, documentation, release management, and version control.
Troubleshoot software, integration, data, and infrastructure issues across the application stack.
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.
Required Skills
2–6 years of relevant professional experience in software development, AI/ML engineering, systems integration, engineering software, automation, or a related field.
Strong proficiency in Python.
Practical experience with REST APIs, API-based integration, and service-oriented architectures.
Familiarity with Model Context Protocol (MCP) or comparable mechanisms for connecting AI applications with tools and enterprise systems.
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 and at least one major cloud platform.
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.
Strong working knowledge of Git and collaborative software development practices.
Understanding of software quality practices including testing, debugging, documentation, and code review.
Ability to work with physical testing data, laboratory reports, and engineering validation information.
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.
Ideal Candidate
The ideal candidate is a pragmatic, hands-on engineer who is comfortable working across software, AI, data, and engineering systems.
They should be able to understand an engineering problem, identify the minimum effective technology needed to solve it, build the solution, and integrate it into a broader technology ecosystem.
They should value working features over unnecessary complexity, reusable solutions over isolated applications, and measurable business or engineering outcomes over technology experimentation.

Work arrangement
No