Industrial Data & AI Engineer

AI in this role

pytorchtensorflow

ml-opscomputer-vision

Location: Bangalore - Indraprastha, India

Thales is a global technology leader trusted by governments, institutions, and enterprises to tackle their most demanding challenges. From quantum applications and artificial intelligence to cybersecurity and 6G innovation, our solutions empower critical decisions rooted in human intelligence. Operating at the forefront of aerospace and space, cybersecurity and digital identity, we’re driven by a mission to build a future we can all trust.

Present in India since 1953, Thales is headquartered in Noida and has other operational offices and sites spread across Delhi, Gurugram, Bengaluru and Mumbai, among others. Over 2200 employees are working with Thales and its joint ventures in India. Since the beginning, Thales has been playing an essential role in India’s growth story by sharing its technologies and expertise in Defence, Aerospace and Cyber & Digital sectors. Thales has two engineering competence centres in India - one in Noida focused on Cyber & Digital business, while the one in Bengaluru focuses on hardware, software and systems engineering capabilities for both the civil and defence sectors, serving global needs. The Group has also established an MRO (Maintenance, Repair & Overhaul) facility in Gurugram to provide comprehensive avionics maintenance and repair services to Indian airlines and support the growth of the local aviation industry.

We are looking for an Industrial Data & AI Engineer to develop and industrialize Data Engineering, AI/ML, Data Science and Computer Vision solutions for global manufacturing operations. The role will transform shop-floor and manufacturing data into scalable solutions for predictive maintenance, quality, process optimization, yield, productivity and operational excellence.

Key Responsibilities:

Build scalable data pipelines, ETL processes, data models and industrial data-lake solutions.
Perform data analysis, feature engineering, ML model development, validation and optimization.
Develop AI/ML solutions for predictive maintenance, anomaly detection, quality prediction, process optimization and forecasting.
Develop Computer Vision solutions for industrial inspection and defect detection.
Industrialize POCs into production-grade AI/ML applications using MLOps/DataOps, CI/CD, model monitoring and versioning.
Deploy solutions across cloud, on-premise and industrial edge environments and integrate shop-floor/OT data with enterprise platforms.
Develop dashboards and analytics supporting manufacturing KPIs such as OEE, FPY, Yield, Cycle Time and Downtime.
Collaborate with Manufacturing, Industrial Engineering, Quality, Maintenance, IT/Data and global production teams.

Key Tools & Technical Skills:

Data/Analytics: Dataiku, Python, SQL, BigQuery, Looker/Data Studio
Data Engineering: Python libraries, Apache Iceberg, Kafka, Composer
AI/ML: TensorFlow, PyTorch, OpenCV, Data Science/ML frameworks
MLOps/DataOps: GitLab, CI/CD, model versioning, monitoring, data/model drift

Qualification:

Bachelor’s/Master’s degree in Computer Science, AI, Data Science, Engineering or related field.
3–8 years in Data Science, AI/ML Engineering or Industrial Analytics.
2+ years delivering production-grade AI/ML solutions.
Manufacturing, Smart Factory, Industrial IoT or Operational Excellence experience preferred.
Strong communication and stakeholder-management skills; English required, French is a plus.

At Thales, we’re committed to fostering a workplace where respect, trust, collaboration, and passion drive everything we do. Here, you’ll feel empowered to bring your best self, thrive in a supportive culture, and love the work you do. Join us, and be part of a team reimagining technology to create solutions that truly make a difference – for a safer, greener, and more inclusive world.

How we rate this

Industrial Data & AI Engineer at Thales rates 10 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ML OpsComputer VisionPyTorchTensorFlow

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
Walk me through a computer vision problem you solved, from raw data to a deployed model.
What are the limits of PyTorch that you've run into, and how did you work around them?
What's a project where you used TensorFlow hands-on?

Adapt your resume

List these exact terms on your resume: ML Ops, Computer Vision, PyTorch, and TensorFlow. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.

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