AI Controls Solutions Engineer

Overview

The role of an AI Controls Solutions Engineer at Phaidra is a multifaceted position that combines expertise in controls engineering, artificial intelligence, and customer support. This overview provides a comprehensive look at the key aspects of the role:

Key Responsibilities

• Serve as a technical expert for Phaidra's customers, collaborating with Technical Account Managers and Data Scientists
• Create facility models for AI Agent ingestion, working with AI research and machine learning teams
• Define AI problems, identify value creation opportunities, and deploy solutions to production
• Collaborate with the product team to define requirements and ensure scalability

Qualifications

• 5-10 years of experience as a controls engineer or programmer
• Expertise in chiller plant fundamentals, thermodynamics, and industrial control systems
• Proficiency in programming languages such as PLC, ladder logic, and block programming
• Deep understanding of data center controls and cooling methodologies
• Strong problem-solving skills and ability to analyze engineered solutions

Additional Skills

• Experience with agile methodologies and rapid prototyping
• Knowledge of cloud platforms and AI/ML applications in controls
• Familiarity with statistics, analytics, and optimization techniques

Work Environment

• 100% remote with a digital nomad policy
• Up to 25% travel for customer meetings and industry events

Compensation and Benefits

• Competitive salary range (varies by location)
• Equity options
• Comprehensive health insurance
• Unlimited paid time off
• Training and development programs This role requires a unique blend of technical expertise, customer-facing skills, and innovative problem-solving abilities, making it an exciting opportunity for those passionate about applying AI in industrial settings.

Core Responsibilities

The AI Controls Solutions Engineer at Phaidra plays a crucial role in bridging the gap between advanced AI technologies and practical industrial applications. Their core responsibilities include:

Technical Implementation and Optimization

• Develop and deploy AI-driven control systems for industrial facilities
• Create facility models for AI Agent ingestion
• Collaborate with AI research teams to develop data-driven algorithms
• Optimize AI algorithms and control strategies for enhanced efficiency

Customer Collaboration and Support

• Work closely with Technical Account Managers and customers
• Define AI problems and identify value creation opportunities
• Assist in deploying solutions to production environments
• Provide technical expertise and support throughout the project lifecycle

Product Development and Scaling

• Contribute to product requirement definitions
• Ensure scalability of solutions across multiple customers
• Collaborate with the product team to enhance Phaidra's offerings

Systems Integration and Programming

• Apply knowledge of industrial control systems and protocols (e.g., BACnet, Modbus)
• Utilize programming skills in PLC, ladder logic, and other relevant languages
• Integrate AI solutions with existing control systems

Performance Analysis and Problem Solving

• Analyze the performance and viability of engineered solutions
• Apply data center-specific knowledge to optimize cooling systems
• Troubleshoot complex issues in AI-controlled environments

Agile Development and Prototyping

• Implement agile methodologies in project execution
• Rapidly prototype new technologies
• Convert prototypes into commercially viable products

Ethical AI Implementation

• Ensure AI systems are designed with fairness and security in mind
• Act as a steward for responsible AI deployment in industrial settings This role demands a unique combination of technical expertise, problem-solving skills, and the ability to translate complex AI concepts into practical, value-driven solutions for customers.

Requirements

To excel as an AI Controls Solutions Engineer at Phaidra, candidates should possess a blend of technical expertise, industry knowledge, and soft skills. Here's a comprehensive breakdown of the requirements:

Educational Background and Experience

• Bachelor's degree in Engineering, Computer Science, or related field (advanced degree preferred)
• 5-10 years of experience as a controls engineer, programmer, or equivalent role

Technical Knowledge

• Expertise in chiller plant fundamentals, configurations, and operations
• Strong understanding of applied thermodynamics, refrigeration systems, and hydronics
• Proficiency in industrial and commercial control systems (e.g., BACnet, Modbus)
• Experience programming control systems using PLC, ladder logic, text, or block programming languages

Data Center Expertise

• Deep understanding of data center-specific controls and cooling methodologies
• Familiarity with air side chilled water and direct expansion cooling systems

AI and Machine Learning Skills

• Experience applying AI and ML in controls applications
• Knowledge of statistics, analytics, and optimization techniques

Cloud and Software Development

• Experience with cloud platforms and service architecture
• Familiarity with markup languages (XML, JSON, YAML)
• Understanding of agile methodologies and rapid prototyping

Soft Skills

• Strong problem-solving abilities
• Excellent communication skills, both written and verbal
• Ability to translate customer needs into technical requirements
• Collaborative mindset for cross-functional teamwork

Additional Requirements

• Willingness to travel up to 25% of the time
• Adaptability to work in a remote environment
• Passion for innovation and continuous learning

Preferred Qualifications

• Experience in troubleshooting complex systems
• Knowledge of energy management and sustainability practices
• Familiarity with data visualization tools
• Understanding of cybersecurity principles in industrial control systems This role requires a unique combination of technical depth, industry knowledge, and the ability to work effectively with both customers and internal teams. The ideal candidate will be passionate about leveraging AI to solve complex industrial challenges and drive innovation in the field of controls engineering.

Career Development

The career path for an AI Controls Solutions Engineer offers diverse opportunities for growth and specialization. Here's an overview of the progression stages:

Entry-Level Positions

• Start with assisting in AI model development, data preparation, and basic implementation of machine learning algorithms
• Require foundational knowledge of AI and machine learning principles
• Proficiency in programming languages like Python and experience with machine learning frameworks

Mid-Level Positions

• Design and implement sophisticated AI models
• Optimize algorithms and contribute to architectural decisions
• Collaborate with stakeholders to align AI solutions with project objectives
• Work closely with Technical Account Managers and Data Scientists
• Create models of facilities for AI agent ingestion
• Define AI problems and understand technology capabilities
• Identify value creation opportunities and deploy solutions to production

Advanced and Senior Roles

• Develop deep expertise in AI and machine learning
• Lead AI projects and mentor junior engineers
• Engage in strategic decision-making
• Stay updated with the latest advancements in AI
• Potentially specialize in research and development or product innovation

Leadership Positions

• Oversee organization-wide AI strategy
• Lead teams of AI engineers
• Guide development of AI projects
• Make critical decisions impacting the organization's AI initiatives

Key Skills and Knowledge Areas

• Controls design engineering and industrial/commercial control systems
• Chiller plant fundamentals and applied thermodynamics
• Data center-specific controls
• Programming control systems (PLC, ladder logic)
• AI and ML applications in controls
• Statistics and analytics
• Markup languages (XML, JSON, YAML)

Career Progression Path

1. Junior AI Controls Solutions Engineer
2. AI Controls Solutions Engineer
3. Senior AI Controls Solutions Engineer
4. Director of AI or similar leadership role

This career path emphasizes the importance of continuous skill development, specialization, and leadership capabilities for advancement in the field of AI Controls Solutions Engineering.

Market Demand

The demand for AI Controls Solutions Engineers is robust and expected to grow significantly in the coming years. Key factors driving this demand include:

Industry Growth

• Global AI engineering market projected to expand from USD 9.2 billion in 2023 to USD 229.61 billion by 2033
• Compound Annual Growth Rate (CAGR) of 38% from 2024 to 2033

Cross-Industry Adoption

• Increasing demand for AI-powered solutions across various sectors:
  • Healthcare
  • Automotive
  • Agriculture
  • Information Technology
  • Finance
  • Industrial automation

Technological Advancements

• Rapid progress in AI technologies such as:
  • Machine learning
  • Natural language processing
  • Computer vision
• Development of automation tools and platforms accelerating AI system deployment

Specialized Industry Needs

• Companies like Phaidra developing AI-powered control systems for industrial applications
• Critical role in operationalizing and scaling AI solutions, particularly in:
  • Data center operations
  • Industrial automation

Job Market Trends

• AI-related jobs among the fastest-growing occupations
• Projected growth of up to 20% by 2031 (U.S. Bureau of Labor Statistics)
• High demand for specialized skills in machine learning and data science

Regional Focus

• North America, especially the U.S., is a dominant region in the AI engineering market
• Driven by presence of tech giants and rapid digital transformation of businesses
• Substantial expansion expected in the AI engineering sector

In summary, the increasing integration of AI across industries, technological advancements, and the growing importance of AI in business operations are fueling the demand for AI Controls Solutions Engineers. This trend is expected to continue, offering promising career prospects in this field.

Salary Ranges (US Market, 2024)

AI Controls Solutions Engineers can expect competitive salaries due to their specialized skill set combining AI expertise with solutions engineering. Here's an overview of the salary landscape:

AI Controls Solutions Engineer Salary Range

• Expected range: $120,000 to $160,000+ per year
• Factors influencing salary:
  • Experience level
  • Location
  • Company size and industry
  • Specific skills and expertise

Salary Breakdown by Experience

1. Entry-level (0-2 years):
   • Range: $90,000 - $120,000
2. Mid-level (3-5 years):
   • Range: $120,000 - $150,000
3. Senior-level (6+ years):
   • Range: $150,000 - $200,000+

Location Impact

• Tech hubs command higher salaries:
  • Silicon Valley
  • Seattle
  • New York City
• Salaries in these areas can exceed $180,000 per year

Industry Comparisons

1. AI Engineer average salary:
   • $119,297 per year
   • Range: $78,000 to $150,000+
2. Solutions Engineer average salary:
   • $131,576 per year
   • Range: $95,482 to $123,284

Additional Compensation

• Many companies offer additional benefits:
  • Stock options
  • Performance bonuses
  • Profit-sharing plans
  • Comprehensive health and retirement benefits

Factors Influencing Salary Growth

• Continuous skill development in AI and machine learning
• Expertise in specific industries or applications
• Leadership and project management capabilities
• Contributions to innovative AI solutions

It's important to note that these figures are approximate and can vary based on individual circumstances, company policies, and market conditions. As the field of AI continues to evolve rapidly, salaries are likely to remain competitive to attract and retain top talent.

Industry Trends

The AI Controls Solutions Engineering field is experiencing rapid evolution, with several key trends shaping its future:

1. Integration of AI in Control Systems: AI is revolutionizing control system design by enabling the creation of accurate nonlinear models from data. This approach combines first-principles modeling with data-driven techniques, enhancing control of complex systems across various industries.
2. AI-Based Reduced Order Models (ROMs): The use of AI-based ROMs is growing, driven by advances in AI technology and computational power. These models reduce computational demands while maintaining accuracy, enabling faster simulations and optimizations.
3. Automation and Efficiency: AI-powered tools are automating routine tasks, allowing engineers to focus on more complex and innovative projects. This automation enhances productivity and streamlines workflows.
4. Predictive Maintenance and Reliability: AI algorithms are being used for predictive maintenance, analyzing real-time data to forecast equipment failures and enable proactive maintenance. This minimizes downtime and extends machinery lifespan.
5. Enhanced Design and Simulation: AI is transforming design processes by using advanced algorithms to optimize performance and cost. It simulates real-world behavior for accurate predictions, enhancing design capabilities.
6. Industrial Automation and Autonomy: Companies are developing AI-powered control systems that enable industrial facilities to automatically learn and improve over time. This involves creating models of facilities, defining AI problems, and deploying AI-driven solutions to production.
7. Addressing the Skills Gap: AI and automation are helping to address the talent gap in engineering by augmenting human capabilities and streamlining workflows. This allows engineers to focus on more complex and creative work.
8. Probabilistic vs. Deterministic Approaches: The integration of AI in industrial control systems requires aligning deterministic approaches with the probabilistic nature of AI/ML algorithms, necessitating structured implementation frameworks. These trends highlight the significant role AI will play in enhancing precision, efficiency, and adaptability in engineering, particularly in control systems, design, simulation, and predictive maintenance.

Essential Soft Skills

For AI Controls Solutions Engineers, a combination of technical expertise and soft skills is crucial for success. Here are the essential soft skills:

1. Communication Skills: Ability to explain complex AI concepts to both technical and non-technical stakeholders through written, verbal, and non-verbal communication.
2. Problem-Solving and Critical Thinking: Skills to break down complex issues, identify potential solutions, and implement them effectively. This involves creative thinking and assessing multiple solutions.
3. Emotional Intelligence and Empathy: Understanding and managing one's own emotions and those of colleagues and clients. Empathy helps in grasping clients' concerns, commercial pains, and visions.
4. Active Listening: Essential for consulting with others, mobilizing teams, and liaising with clients. This involves using body language, verbal prompts, and insightful questions.
5. Negotiation and Conflict Resolution: Necessary for securing approval for methods, keeping project teams cohesive, and finding compromises to project conflicts.
6. Adaptability and Positive Attitude: Crucial when dealing with delays, problems, or the need for additional knowledge. This helps in managing project issues effectively.
7. Teamwork and Interpersonal Skills: Ability to collaborate, motivate team members, and ensure smooth interactions with all stakeholders involved in the project.
8. Self-Awareness and Continuous Learning: Recognizing the need for additional knowledge and skills, and taking the initiative to acquire them to stay updated with the latest technologies. Mastering these soft skills enables AI Controls Solutions Engineers to navigate the complexities of their role, enhance team collaboration, and deliver more effective solutions.

Best Practices

Implementing AI in control systems engineering requires adherence to several best practices to ensure efficiency, reliability, and effectiveness:

1. Clear Planning and Implementation:
   • Develop a phased implementation plan for gradual AI solution introduction
   • Create a control systems philosophy outline to assess current state, goals, and capabilities
2. Data Quality and Management:
   • Ensure high-quality, relevant data for training AI algorithms
   • Optimize data collection by understanding sensor relationships and existing metrics
3. Pipeline Design and Management:
   • Design idempotent and repeatable pipelines for consistent results
   • Automate pipeline runs and make them observable for monitoring performance
4. Integration with Existing Systems:
   • Use flexible tools for data ingestion and processing
   • Integrate AI solutions with existing infrastructure (SCADA, MES, PLCs, ERP)
5. Transparency and Accountability:
   • Present AI as an augmentation tool, not a replacement for human engineers
   • Be clear about AI capabilities and limitations
6. Testing and Validation:
   • Test pipelines across different environments before production
   • Implement AI models gradually, starting with specific areas like predictive maintenance
7. Project Management and Task Automation:
   • Use AI to streamline tasks and enhance efficiency
   • Leverage AI for natural language processing in documentation By following these best practices, control systems engineers can effectively integrate AI into their workflows, enhancing efficiency, reliability, and overall performance.

Common Challenges

Implementing AI solutions in control systems engineering presents several challenges that need to be addressed:

1. Skill Gaps and Learning Curves: The rapid advancement of AI technology requires continuous learning and acquisition of new skills. This gap can lead to inefficiencies and errors in AI implementation.
2. Ethical Considerations and Biases: AI systems can perpetuate biases if trained on biased or incomplete data. Ensuring ethical decision-making is crucial to prevent discriminatory outcomes.
3. Data Privacy and Security: Handling large datasets with sensitive information raises privacy and security concerns. Robust security systems and data protection protocols are essential.
4. Balancing AI Dependence: Over-reliance on AI tools can impede critical thinking and problem-solving skills. AI should augment human capabilities rather than replace them entirely.
5. Integration with Legacy Systems: Incorporating AI into existing systems can be complex and disruptive. Using middleware can help bridge the gap between old and new systems.
6. Cost and Resource Constraints: AI integration and maintenance require significant investment in hardware, software, and personnel with specialized expertise.
7. Data Quality and Scalability: Ensuring high-quality, diverse datasets is critical for AI performance. Scaling up AI systems requires optimized cloud-based architectures.
8. Regulatory Compliance: Navigating the evolving regulatory landscape for AI is challenging. Building AI systems with compliance in mind is essential.
9. Transparency and Explainability: Developing AI models with explanation capabilities and ensuring transparency in decision-making processes is crucial for stakeholder trust and regulatory compliance. Addressing these challenges through continuous learning, data quality improvement, ethical frameworks, and careful integration with existing systems can help maximize the benefits of AI while minimizing its drawbacks.