Implementation Engineer

UptimeAI Inc.; India; In loco; INR 1.200.000 - 1.800.000; Tempo pieno

Descrizione del lavoro

UptimeAI Inc. in India is seeking an Implementation Engineer to drive model-building and product delivery during customer implementations.

You will collaborate with Project Managers and Technical Consultants to deploy scalable AI solutions, validate models, and ensure outputs meet client needs.

The role emphasizes hands-on analytics deployment, documentation, automation of repetitive tasks, and sharing domain knowledge to continuously raise the quality bar.

Competenze

• 2+ years hands-on experience deploying analytics solutions, ML, or rule-based models.
• Ability to define and execute validation steps to ensure model accuracy and output reliability.
• Experience documenting processes and contributing to repeatable workflows.

Mansioni

• Understand technical scope and support discovery, requirement gathering, and model-building preparation for efficient development.
• Review and map tags by reviewing P&IDs, PFDs, and performance & efficiency calculations.
• Deploy models into customer environments aligned with their specific needs.
• Conduct rigorous validation and quality assurance on all deployed models.
• Collaborate closely with Project Managers and Technical Consultants to meet project timelines and quality benchmarks.
• Provide structured feedback to the Product team to improve functionality, user experience, and deployment speed.
• Identify opportunities to automate repetitive tasks and increase efficiency across the model-building workflow.
• Develop and share domain knowledge and implement best practices to continuously raise the quality bar.

Descrizione del lavoro

Implementation Engineer

About this role:

UptimeAI is looking for a highly motivated and detail-oriented Implementation Engineer to join our growing team in India. This role is critical to the success of our customer implementations and is focused on the core technical responsibility of model building and product delivery during the implementation process.

You will work closely with Project Managers and Technical Consultants to deliver high-quality, scalable solutions that help our industrial clients achieve measurable value from our AI-powered platform.

Who You Are:

• Understand technical scope and Support discovery, requirement gathering, and model-building preparation for efficient development.

• Experience in reviewing and understanding P&IDs, PFDs, and performance & efficiency calculations to perform accurate and comprehensive tag mapping

• Deploy models into customer environments aligned with their specific needs.

• Conduct rigorous validation and quality assurance on all deployed models.

• Collaborate closely with Project Managers and Technical Consultants to meet project timelines and quality benchmarks.

• Provide structured feedback to the Product team to improve functionality, user experience, and deployment speed.

• Identify opportunities to automate repetitive tasks and increase efficiency across the model-building workflow.

• Develop and share domain knowledge and implement best practices to continuously raise the quality bar.

Qualifications:

Must Have:

• Hands-on Experience: 2+ years of hands-on Experience deploying analytics solutions, ML, or rule-based models.

• Quality Assurance and Validation Skills: Ability to define and execute validation steps to ensure model accuracy and output reliability.

• Ability to Work in a Structured, Scalable Way: Familiar with documenting processes, following checklists, and contributing to repeatable workflows.

Stong Plus:

• Experience in Industrial AI/ML Solutions: Previous implementation of work in AI/ML platforms in manufacturing, oil & gas, or chemical environments.

• Understanding Reliability Engineering Concepts: Exposure to RCM, FMEA , condition monitoring, or predictive maintenance.

• Experience with Tag Mapping and Data Integration: Proficiency in reviewing and mapping sensor tags from control systems like DCS/SCADA/Historians to platform inputs.

Success Metrics:

• Quality and accuracy of deployed models

• Timely and successful deployment across multiple implementations

• Process improvement Outcomes (Reduction in deployment time)