Digital Engineering - SW & AI

Digital Engineering - SW & AI

Digital Engineering - SW & AI
At TE, you will unleash your potential working with people from diverse backgrounds and industries to create a safer, sustainable and more connected world.
Determines problems within specific systems and provides solutions, including designing new systems, platforms or test applications.
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.

Values: Integrity, Accountability, Inclusion, Innovation, Teamwork
SURVEY NOS.11/1, 11/2, 11/4, 11/5, 23/4, 24/1BANGALORE, Karnātaka 560048India

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