The Randstad companies are responsible for finding and contracting talent for project roles at Philips. If you are selected for a role you will be contracted by the broker or employed by a Randstad company, and will not be an employee of Philips.
Philips
AI Engineer
Posted
Mar 12, 2026
Project ID:
PHIAJP00003519
Location
Bangalore
Hours/week
40 hrs/week
Timeline
1 year
Starts: Apr 6, 2026
Ends: Apr 8, 2027
Payrate range
Unknown
Application Deadline: May 30, 2026 4:30 AM
Job Posting Title - AI Engineer
Job Description Summary
The Data and AI Engineer 50 is responsible for participating in AI DevOps activities, contributing to the streamlining of development, testing, deployment, and monitoring processes, working under direct supervision. The role collaborates extensively on technical requirements, designs, and quality standards to ensure the robustness and scalability of AI solutions. The role integrates machine learning models into end products, handles data processing activities to maintain data integrity, and trains AI systems for optimal performance. The role contributes to team activities, provides guidance and support to junior members, and actively participates in the development process from requirements gathering to quality assurance.
Job Responsibilities:
• Collaborates extensively on AI DevOps activities, leveraging multiple tool sets at the solution level to streamline development, testing, deployment, and monitoring processes with moderate complexity.
• Participates in discussions related to technical requirements, designs, and quality standards, providing inputs to ensure the robustness, scalability, and maintainability of AI solutions, working under direct supervision.
• Integrates deployable versions of machine learning models developed by data scientists into end products, ensuring compatibility, performance, and reliability of all system components.
• Handles data processing activities, including data cleansing, validation, and verification, to ensure the integrity and quality of data used for analysis and model training, with a focus on continuous improvement.
• Trains and retrains AI systems as necessary to adapt to changing data distributions, business requirements, and performance objectives, optimizing model performance through iterative refinement.
• Performs statistical analysis and fine-tuning of AI models using test results, hypothesis testing, and validation techniques to validate model assumptions, identify areas for improvement, and optimize model parameters.
• Contributes to activities within the DevOps team, providing guidance, mentorship, and support to junior team members, and facilitating collaboration and knowledge sharing across the team.
• Participates in the AI development process, working in pairing mode with equal team members, and actively contributing to requirements gathering, design discussions, code reviews, and quality assurance activities.
• Interacts with business, market, and IT stakeholders to formulate clear and actionable requirements for AI solutions, ensuring alignment with business goals, user needs, and technical capabilities.
• Ensures the quality of data and AI solutions developed, conducting thorough testing, validation, and verification activities to identify and address defects, errors, and performance issues before deployment.
Job Description Summary
The Data and AI Engineer 50 is responsible for participating in AI DevOps activities, contributing to the streamlining of development, testing, deployment, and monitoring processes, working under direct supervision. The role collaborates extensively on technical requirements, designs, and quality standards to ensure the robustness and scalability of AI solutions. The role integrates machine learning models into end products, handles data processing activities to maintain data integrity, and trains AI systems for optimal performance. The role contributes to team activities, provides guidance and support to junior members, and actively participates in the development process from requirements gathering to quality assurance.
Job Responsibilities:
• Collaborates extensively on AI DevOps activities, leveraging multiple tool sets at the solution level to streamline development, testing, deployment, and monitoring processes with moderate complexity.
• Participates in discussions related to technical requirements, designs, and quality standards, providing inputs to ensure the robustness, scalability, and maintainability of AI solutions, working under direct supervision.
• Integrates deployable versions of machine learning models developed by data scientists into end products, ensuring compatibility, performance, and reliability of all system components.
• Handles data processing activities, including data cleansing, validation, and verification, to ensure the integrity and quality of data used for analysis and model training, with a focus on continuous improvement.
• Trains and retrains AI systems as necessary to adapt to changing data distributions, business requirements, and performance objectives, optimizing model performance through iterative refinement.
• Performs statistical analysis and fine-tuning of AI models using test results, hypothesis testing, and validation techniques to validate model assumptions, identify areas for improvement, and optimize model parameters.
• Contributes to activities within the DevOps team, providing guidance, mentorship, and support to junior team members, and facilitating collaboration and knowledge sharing across the team.
• Participates in the AI development process, working in pairing mode with equal team members, and actively contributing to requirements gathering, design discussions, code reviews, and quality assurance activities.
• Interacts with business, market, and IT stakeholders to formulate clear and actionable requirements for AI solutions, ensuring alignment with business goals, user needs, and technical capabilities.
• Ensures the quality of data and AI solutions developed, conducting thorough testing, validation, and verification activities to identify and address defects, errors, and performance issues before deployment.