New opportunity

HDNU Controls- AI Integration System Architect

GE Vernova · Hyderabad, India

About this role

GE Vernova is seeking a Senior Engineer (Controls, Technology, and Hardware) at the SPB3 level for its Gas Power Controls team in Hyderabad, India. This role is instrumental in supporting the integration and deployment of advanced AI/ML solutions across the HDNU control team to enhance performance, productivity, reliability, efficiency, safety, and decarbonization efforts. Relocation assistance is provided.

Responsibilities

Lead the design and governance of AI/ML-integrated control system solutions for HDNU controls value stream.
Lead and support HDNU controls copilot governance and develop the implementation strategy across the domain.
Act as a trusted technical advisor to Copilot teams and internal stakeholders across GT, ST, HRSG, and BOP.
Present architecture options and trade-offs clearly to HDNU system architects and executive audiences.
Contribute to reusable accelerators, reference architectures, and platform standards.
Ensure GenAI solutions move efficiently from PoC to production with clear architectural standards, security, cost management, and enterprise governance.
Translate emerging AI/ML technologies into production-ready HDNU control applications and support pilot projects.
Develop training plans and conduct training for the HDNU team on Controls copilot solutions.
Drive the AI strategy for the HDNU value stream, identifying and championing AI/ML deployment opportunities in pilot projects.
Develop execution plans, implementation strategies, and track cost savings from solutions while owning quality documents in Confluence.
Collaborate with mechanical, electrical, systems, accessories, and instrumentation engineers for holistic control solutions.
Scout for new ideas, technologies, and methodologies, promoting an innovation mindset and mentoring engineers.
Present and drive solution roadmaps and technical strategies to senior leadership and stakeholders.

Required Qualifications

Bachelor’s degree in engineering, I&C, Chemistry, Electrical, Mechanical, Computer Science, or related engineering.
At least 12 years of experience in a combined cycle domain engineering capacity, with a focus on AI/ML and control systems.
Minimum 5 years of experience in combined cycle projects as an added advantage.
Strong oral and written communication skills.
Exposure to enterprise AI governance, compliance, and risk management frameworks.
Experience contributing to enterprise platform strategy or AI operating models.
Expertise in AI/ML methodologies, including predictive modelling, anomaly detection, deep learning, and reinforcement learning.
Demonstrated leadership, interpersonal skills, and strong mentorship capabilities.
Deep understanding of modern control system architectures and industrial communication protocols.
Proficiency in programming languages commonly used for AI/ML (e.g., Python, C, C++) and control systems (e.g., C++, Ladder Logic, Structured Text) as an added advantage.
Exceptional strategic thinking, problem-solving, and analytical skills.
Demonstrated ability to influence, collaborate, and communicate effectively across diverse technical and business teams.

Preferred Characteristics:

Strong leadership skills to keep the team focused and innovating through dynamic business cycles.
System integration mindset for meeting customer demands.
Excellent communication skills with the ability to sell technology through simple and clear messaging.
Lean mindset, acting on feedback, and driving productivity initiatives for business results and personal engineering satisfaction.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI/MLControl SystemsPythonCC++Ladder LogicStructured TextDeep LearningReinforcement LearningPredictive ModellingAnomaly DetectionGenerative AIScaled Agile FrameworkConfluence

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

AI/ML Integration and Architecture Governance
Lead the design, governance, and architecture of AI/ML-integrated control system solutions across the HDNU controls value stream, ensuring GenAI solutions move efficiently from Proof of Concept to production with clear standards.
Cross-Functional Collaboration and Copilot Strategy
Act as a trusted technical advisor to Copilot teams and stakeholders across GT, ST, HRSG, and BOP, while collaborating closely with mechanical, electrical, systems, accessories, and instrumentation engineers.
Strategic Roadmap and Execution Planning
Drive AI strategy and execution plans for the HDNU value stream, identifying deployment opportunities, establishing cost-savings metrics, and presenting technical strategies and trade-offs clearly to executive audiences.
Technical Mentorship and Training Development
Develop training plans and conduct training sessions for the HDNU team on controls copilot solutions, while promoting an innovation mindset and mentoring engineers to foster continuous learning.

Likely challenges to think through

Bridging Advanced AI/ML with Traditional Industrial Control Systems
Difficulty: Integrating modern AI/ML methodologies (such as deep learning and reinforcement learning) with rigorous, mission-critical industrial control system architectures and protocols (like Ladder Logic and Structured Text) without compromising system safety or reliability. Preparation: Review modern control system architectures, industrial communication protocols, and strategies for safely wrapping AI components around deterministic control loops.
Managing the Transition from Proof of Concept to Production Governance
Difficulty: Moving Generative AI and ML solutions efficiently past the pilot stage while aligning with enterprise AI governance, compliance, security, and cost management frameworks. Preparation: Prepare to discuss enterprise AI operating models, governance frameworks, and methods for tracking ROI and cost management in production AI environments.
Driving Cross-Domain Alignment Across Diverse Stakeholder Groups
Difficulty: Aligning mechanical, electrical, systems, and instrumentation engineering teams with software-driven AI strategies while clearly articulating complex technical trade-offs to senior leadership. Preparation: Formulate frameworks for cross-functional technical communication, translating complex algorithmic concepts into clear business values and operational metrics.

Practice your answers

Technical practice

Designing AI/ML-Integrated Control System Architectures

What to coverDiscuss how you would approach designing a governance and architectural framework for integrating AI/ML models into industrial control systems for combined cycle power plants. Candidates should address the balance between deterministic real-time control constraints and probabilistic machine learning outputs, ensuring safety, reliability, and low latency.

Transitioning GenAI Solutions from PoC to Production

What to coverWhat architectural standards and security measures are essential when moving Generative AI copilot solutions from a proof-of-concept stage to full production deployment? Focus your answer on architectural consistency, cost management, enterprise compliance, and mitigating drift or hallucinations in industrial environments.

Applying Predictive Modeling and Anomaly Detection in Power Controls

What to coverExplain how predictive modeling and anomaly detection methodologies can be implemented within gas power controls (such as GT, ST, HRSG, or BOP) to enhance operational efficiency and decarbonization. Discuss data ingestion pipelines, feature engineering from industrial communication protocols, and handling noisy sensor streams.

Leveraging Deep Learning and Reinforcement Learning for System Optimization

What to coverIn what scenarios would you choose deep learning versus reinforcement learning for optimizing power plant control loops or fast power grid stabilization? Address reward function design, simulation environments, safety bounds enforcement, and verification challenges in physical systems.

Bridging High-Level AI Languages with Low-Level Control Logic

What to coverGiven that the role involves proficiency in Python, C, C++, and industrial control languages like Ladder Logic and Structured Text, how do you manage the software interface between high-level AI algorithms and low-level programmable logic controllers (PLCs)? Address execution timing, memory management, and deterministic safety checks.

Enterprise AI Governance and Risk Management Frameworks

What to coverHow would you establish and enforce enterprise AI governance, risk management, and compliance frameworks across a large multi-stakeholder engineering organization? Emphasize auditability, version control for models, documentation standards (such as in Confluence), and regulatory compliance.

Developing Reusable Accelerators and Reference Architectures

What to coverDescribe your strategy for creating reusable accelerators and platform standards to prevent code duplication across different value streams and engineering teams. Focus on modular design patterns, API contracts between AI services and control frameworks, and maintaining platform scalability.

Cross-Functional Collaboration Across Engineering Disciplines

What to coverHow do you effectively collaborate with mechanical, electrical, systems, accessories, and instrumentation engineers to ensure holistic control solutions? Discuss translation of complex algorithmic requirements into actionable mechanical and electrical constraints.

Communicating Complex Technical Trade-offs to Executive Audiences

What to coverWhen presenting architecture options and strategic roadmaps to executive leadership and system architects, how do you frame complex technical trade-offs? Highlight methods for articulating business value, cost savings, and risk mitigation clearly and concisely.

Behavioural practice

Leading AI Adoption and Change Management in an Engineering Team

What to coverDescribe a situation where you spearheaded the adoption of a new AI technology or framework within a traditional engineering domain. Use the STAR method to explain how you managed resistance, developed training plans, and secured buy-in from cross-functional stakeholders.

Navigating Conflicting Priorities Between Innovation and System Reliability

What to coverShare an experience where you had to balance aggressive AI innovation goals with strict industrial safety and reliability constraints. Outline the situation, the specific technical or operational challenges you faced, the actions you took to resolve the conflict, and the resulting impact on system performance.

Mentoring Engineers and Fostering a Continuous Learning Culture

What to coverProvide an example of how you mentored junior or senior engineers to bridge the gap between traditional domain engineering and modern AI/ML methodologies. Detail your approach to capability building, fostering an innovation mindset, and driving continuous improvement.

Driving Productivity Initiatives Using Lean Principles and Agile Frameworks

What to coverDescribe a time when you applied Lean principles or worked within a Scaled Agile Framework (SAFe) environment to streamline a complex engineering workflow or deployment pipeline. Detail your role, the productivity metrics you tracked, and how you acted on feedback to deliver results.

Sharpen your knowledge

Topics to prepare

Enterprise AI Governance and Compliance Frameworks
Conceptual Topic. Exercise: Review enterprise risk management frameworks, compliance guidelines, and AI operating models to establish standard guardrails for GenAI deployment. Relevance: Directly aligns with the required qualification for exposure to enterprise AI governance, compliance, and risk management frameworks.
Modern Control System Architectures and Industrial Protocols
Conceptual Topic. Exercise: Study modern control system architectures, combined cycle power plant components (GT, ST, HRSG, BOP), and industrial communication protocols. Relevance: Essential for designing and governing AI/ML-integrated control solutions across the HDNU value stream.
AI/ML Methodologies for Industrial Applications
Conceptual Topic. Exercise: Examine predictive modeling, anomaly detection, deep learning, and reinforcement learning applications in industrial systems. Relevance: Directly supports the core requirement for expertise in these AI/ML methodologies to enhance performance, reliability, and safety.

Skills to revise

Programming and Control Logic (Python, C++, Ladder Logic, Structured Text)
Hands-on Skill. Exercise: Practice writing and reviewing code snippets in Python and C++ for AI/ML integration alongside Ladder Logic or Structured Text for control systems. Relevance: Aligns with the preferred/added-advantage programming languages for bridging AI/ML and control systems.
Scaled Agile Framework (SAFe) and Lean Principles
Hands-on Skill. Exercise: Outline how to manage AI integration sprints, value stream mapping, and continuous improvement initiatives within a SAFe environment. Relevance: Directly tied to the working environment and Lean principles highlighted in the job summary.
Technical Architecture Communication and Documentation
Hands-on Skill. Exercise: Draft a sample reference architecture and Confluence documentation explaining GenAI PoC-to-production pipelines for executive stakeholders. Relevance: Fulfills the responsibility of creating quality documents and clearly presenting architecture options and trade-offs.

Your preparation checklist

Compile Evidence of AI and Control Systems Experience
Gather documentation, case studies, or portfolio notes demonstrating your 12+ years of combined cycle domain engineering experience, highlighting specific projects where you bridged advanced AI/ML methodologies (such as predictive modeling, anomaly detection, deep learning, or reinforcement learning) with industrial control systems.
Develop an AI-Integrated Control Architecture Reference Model
Create a sample system architecture diagram and accompanying brief outlining how to transition Generative AI and machine learning solutions from proof-of-concept (PoC) to production in industrial settings, incorporating enterprise governance, security, and cost-management standards.
Prepare Code and Logic Walkthrough Samples
Prepare code examples or technical explanations showcasing your proficiency across AI/ML languages (Python, C, C++) and control system languages (C++, Ladder Logic, Structured Text), demonstrating how high-level intelligence interfaces safely with deterministic control loops.
Draft Governance and Copilot Implementation Strategy Notes
Formulate a structured strategy document for controls copilot governance across value streams (GT, ST, HRSG, BOP), detailing how to establish platform standards, training plans, and quality documentation (e.g., for Confluence) within a Scaled Agile Framework (SAFe) environment.
Prepare STAR Examples for Cross-Functional Leadership and Mentorship
Outline specific STAR (Situation, Task, Action, Result) stories highlighting your experience leading cross-functional teams (mechanical, electrical, instrumentation), mentoring junior/senior engineers, and communicating complex technical trade-offs to executive audiences.
Formulate Thoughtful Questions for the Employer
Develop 4-5 targeted questions regarding GE Vernova's HDNU AIML journey for 2030, current platform standards, cross-functional collaboration dynamics across global engineering teams, and expectations for driving decarbonization and productivity initiatives using Lean principles.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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