Lead AR Engineer

Findernest Software Services Pvt Ltd
Par
1 year ago
Applications closed

Related Jobs

View all jobs

Lead Machine Learning Engineer, Gen AI

Lead Data Scientist - eCommerce

Lead Data Scientist

Lead Data Scientist - Marketing Science

Lead Data Scientist

Lead MLOps Engineer

LOCATION:FAIRWAY BUSINESS PARKBangalore
Mode:Onsite
TheLead AR Engineer plays a crucial role in our organizationoverseeing the design development and implementation of augmentedreality (AR) solutions. They will lead a team of AR engineersensuring the successful delivery of AR projects while collaboratingwith crossfunctional teams to drive innovation and exceed clientexpectations.

KeyResponsibilities

  • Collaborate withinternal teams and clients to design and develop AR/IoT solutionsusing PTC platforms such as ThingWorx.
  • Developcustom AR applications and IoT solutions integrating sensorsactuators and other hardware components using PTC tools andtechnologies.
  • Integrate APIs and data sourcesinto AR/IoT systems to enable realtime data tracking analytics anduser authentication.
  • Design and implement userinterfaces and interactions in AR applications leveraging PTCs bestpractices and UX research.
  • Develop unit tests and quality validation proceduresto ensure reliable and highquality AR/IoTproducts.
  • Troubleshoot and debug issues inAR/IoT systems ensuring optimal performance and userexperience.
  • Stay uptodate with advancements inPTC platforms and technologies as well as industry trends in AR andIoT.
  • Collaborate with crossfunctional teamsincluding developers engineers and designers to ensure successfulproject implementation.
  • Provide technicalguidance and support to clients and stakeholders during theimplementation and deployment of AR/IoTsolutions.
  • Document system designsconfigurations and processes for future reference and knowledgesharing.
SkillsRequired:
  • Proficiency in PTC platforms particularly ThingWorx for AR and IoTdevelopment.
  • Strong understanding of IoTconcepts protocols and architectures.
  • Experience in developing AR applications using PTC Vuforia orsimilar AR development frameworks.
  • Familiaritywith sensor integration data collection and analytics in IoTsystems.
  • Knowledge of PTCs industrialconnectivity solutions and protocols such as MQTT andOPCUA.
  • Proficiency in programming languagessuch as JavaScript C or C# for developing AR/IoTapplications.
  • Experience with cloud platformssuch as Microsoft Azure or AWS for deploying and managing AR/IoTsolutions.
  • Strong problemsolving and debuggingskills for troubleshooting complex AR/IoTsystems.
  • Excellent communication andcollaboration abilities to work effectively with crossfunctionalteams and stakeholders.

RequiredQualifications

  • B.E./B.Tech. (ComputerScience) or a related field.
  • 8 11 years ofrelevant experience in developing AR/IoT solutions using PTCplatforms.
  • Demonstrated experience inimplementing AR/IoT projects from concept todeployment.
  • Experience in working withindustrial or enterprise IoT applications ispreferred.

ar/iot development,sensorintegration,data analysis,iot,mqtt,team leadership,cloudplatforms,ptc creo,data collection,industrial connectivitysolutions,vuforia,iot concepts,machine learning,debugging,python,arsystem,ptcrb,thingworx,programming languages,ptcplatforms,analytics

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

Jobs

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

Education

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.