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Position: NDT Applications Engineer – PAUT ( Corrosion Mapping/ Weld Inspection / PWI/Advance FMC-TFM)

Location: Noida

Job Type: Full-time

Experience Level: Mid-Level / Senior-Level

Industry: Non-Destructive Testing (NDT), PAUT.

We are specifically looking for candidates with hands-on experience as an NDT Senior Engineer; only those with relevant NDT expertise should apply.

We are specifically looking for candidates with hands-on experience as an NDT Senior Engineer; only those with relevant NDT expertise should apply."

Job Summary

We are seeking a highly skilled NDT-Engineer with expertise in Ultrasonic Testing (UT) and Phased Array Ultrasonic Testing (PAUT) for robotic integration. This role involves coordinating with software and control system teams to integrate UT & PAUT ( Corrosion Mapping/Weld Inspection) into robotic NDT systems, ensuring optimal inspection performance.

The engineer will focus on sensor selection, ultrasonic parameter optimization, calibration, and data interpretation, while the software team handles control algorithms and motion planning. The ideal candidate should have strong experience in NDT automation, probe and frequency selection, phased array data acquisition, and defect characterization.

Key Responsibilities

1. NDT Inspection & Signal Optimization

• Optimize probe selection, wedge design, and beam focusing to achieve high-resolution imaging.

• Define scanning techniques (sectorial, linear, and compound scans) to detect various defect types.

• Analyse UT & PAUT signals, ensuring accurate defect detection, sizing, and characterization.

• Implement Time-of-Flight Diffraction (TOFD) and Full Matrix Capture (FMC) techniques to enhance detection capabilities.

• Address electromagnetic interference (EMI) and signal noise issues affecting robotic UT/PAUT.

• Develop procedures for coupling enhancement, including the use of water column, dry coupling, and adaptive surface-following mechanisms for robotic probes.

• Evaluate attenuation, beam divergence, and wave mode conversion for different material types.

• Work with AI-based defect recognition systems to automate data processing and anomaly detection.

• Test different scanning configurations for challenging surfaces, curved geometries, and weld seams.

• Optimize gain, pulse repetition frequency (PRF), and filtering settings to ensure the highest signal clarity.

• Implement phased array data interpretation techniques to differentiate between false indications and real defects.

• Develop and refine automated thickness gauging algorithms for robotic NDT systems.

• Ensure the compatibility of PAUT imaging with robotic motion constraints to avoid signal distortion.

2. NDT-Integration for Robotics (UT & PAUT)

•Select, integrate, and optimize ultrasonic transducers and phased array probes for robotic inspection systems.

•Define NDT scanning parameters (frequency, angle, probe type, and scanning speed) for robotic UT/PAUT applications.

•Ensure seamless coordination with control system and software teams for planning and automation.

•Work with robotic hardware teams to mount, position, and align UT/PAUT probes accurately.

•Conduct system calibration and validate UT/PAUT performance on robotic platforms.

3. Data Analysis & Reporting

•Interpret PAUT sectorial scans, full matrix capture (FMC), and total focusing method (TFM) data.

•Assist the software team in processing PAUT data for defect characterization and AI-based analysis.

•Validate robotic UT/PAUT inspection results and generate detailed technical reports.

•Ensure compliance with NDT standards (ASME, ISO 9712, ASTM, API 510/570) for ultrasonic inspections.

4. Coordination with Software & Control System Teams

•Work closely with the software team to define scan path strategies and automation logic.

•Collaborate with control engineers to ensure precise probe movement and stability.

•Provide technical input on robotic payload capacity, motion constraints, and scanning efficiency.

•Assist in integration of AI-driven defect recognition for automated data interpretation.

5. Field Deployment & Validation

•Supervise robotic UT/PAUT system trials in real-world inspection environments.

•Ensure compliance with safety regulations and industry best practices.

•Support on-site troubleshooting and optimization of robotic NDT performance.

•Train operators on robot-assisted ultrasonic testing procedures.

Required Qualifications & Skills

1. Educational Background

•Master’s Degree in Metallurgy/NDT/Mechanical.

•ASNT-Level II/III, ISO 9712, PCN, AWS CWI, or API 510/570 certifications in UT & PAUT preferred.

2. Technical Skills & Experience

•3-10 years of experience in Ultrasonic Testing (UT) and Phased Array Ultrasonic Testing (PAUT).

•Strong understanding of probe selection, frequency tuning, and phased array beamforming.

•Experience with NDT software

•Knowledge of electromagnetic shielding, signal integrity, and noise reduction techniques in ultrasonic systems.

•Ability to collaborate with software and control teams for robotic NDT development.

3. Soft Skills

•Strong problem-solving and analytical abilities.

•Excellent technical communication and coordination skills.

•Ability to work in cross-functional teams with robotics, software, and NDT specialists.

•Willingness to travel for on-site robotic NDT deployments.

Work Conditions

•Lab – Hands-on testing and robotic system deployment.

•Flexible Work Hours – Based on project

Benefits & Perks

•Competitive salary & performance incentives.

•Exposure to cutting-edge robotic and AI-driven NDT innovations.

•Training & certification support for career growth.

•Opportunities to work on pioneering robotic NDT projects.

Review Criteria:

• Strong MLOps profile
• 8+ years of DevOps experience and 4+ years in MLOps / ML pipeline automation and production deployments
• 4+ years hands-on experience in Apache Airflow / MWAA managing workflow orchestration in production
• 4+ years hands-on experience in Apache Spark (EMR / Glue / managed or self-hosted) for distributed computation
• Must have strong hands-on experience across key AWS services including EKS/ECS/Fargate, Lambda, Kinesis, Athena/Redshift, S3, and CloudWatch
• Must have hands-on Python for pipeline & automation development
• 4+ years of experience in AWS cloud, with recent companies
• (Company) - Product companies preferred; Exception for service company candidates with strong MLOps + AWS depth

Preferred:

• Hands-on in Docker deployments for ML workflows on EKS / ECS
• Experience with ML observability (data drift / model drift / performance monitoring / alerting) using CloudWatch / Grafana / Prometheus / OpenSearch.
• Experience with CI / CD / CT using GitHub Actions / Jenkins.
• Experience with JupyterHub/Notebooks, Linux, scripting, and metadata tracking for ML lifecycle.
• Understanding of ML frameworks (TensorFlow / PyTorch) for deployment scenarios.

Job Specific Criteria:

• CV Attachment is mandatory
• Please provide CTC Breakup (Fixed + Variable)?
• Are you okay for F2F round?
• Have candidate filled the google form?

Role & Responsibilities:

We are looking for a Senior MLOps Engineer with 8+ years of experience building and managing production-grade ML platforms and pipelines. The ideal candidate will have strong expertise across AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows. You will work closely with data science, data engineering, and platform teams to operationalize and scale ML models in production.

Key Responsibilities:

• Design and manage cloud-native ML platforms supporting training, inference, and model lifecycle automation.
• Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue).
• Containerize and deploy ML workloads using Docker, EKS, ECS/Fargate, and Lambda.
• Develop CI/CT/CD pipelines integrating model validation, automated training, testing, and deployment.
• Implement ML observability: model drift, data drift, performance monitoring, and alerting using CloudWatch, Grafana, Prometheus.
• Ensure data governance, versioning, metadata tracking, reproducibility, and secure data pipelines.
• Collaborate with data scientists to productionize notebooks, experiments, and model deployments.

Ideal Candidate:

• 8+ years in MLOps/DevOps with strong ML pipeline experience.
• Strong hands-on experience with AWS:
• Compute/Orchestration: EKS, ECS, EC2, Lambda
• Data: EMR, Glue, S3, Redshift, RDS, Athena, Kinesis
• Workflow: MWAA/Airflow, Step Functions
• Monitoring: CloudWatch, OpenSearch, Grafana
• Strong Python skills and familiarity with ML frameworks (TensorFlow/PyTorch/Scikit-learn).
• Expertise with Docker, Kubernetes, Git, CI/CD tools (GitHub Actions/Jenkins).
• Strong Linux, scripting, and troubleshooting skills.
• Experience enabling reproducible ML environments using Jupyter Hub and containerized development workflows.

Education:

• Master’s degree in computer science, Machine Learning, Data Engineering, or related field.