TE Connectivity
2 weeks ago
Digital Engineering - SW & AI
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Company information
- Company
- TE Connectivity
- Location
- India, Karnataka, Bangalore India
- Posted
- 2 weeks ago
Job description
This job is with TE Connectivity, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.
At TE, you will unleash your potential working with people from diverse backgrounds and industries to create a safer, sustainable and more connected world. Job OverviewDetermines 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.
- 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.
Required skills
- debugging
- teamwork
- engineering
- documentation
- manufacturing
- problem-solving
- analytical skills
- software development
- generative ai
- react
- sql
- inclusion
- industrial automation
- python
- aws
- automation
- git
- innovation
- testing
- evaluation
- azure
- machine learning
- nosql databases
- iot
- rest apis
- cloud computing
- ai agents
- mcp
- integrity
- deep learning
- code review
- engineering software
- ux design
- deployment
- accountability
- digital twins
- simulation
- database design
- inference
- service-oriented architectures
- systems integration
- software quality practices
- model training
- edge computing
- separation of concerns
- llm-based applications
- cad/cae
- reusable components
- collaborative software development
- modular software architecture
- process data
- machine data
- ai/ml engineering
- model-based engineering
- relational data modeling
- distributed application architectures
- api-based integration
- ai-enabled workflow automation
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