Computational Solutions Architect

Airswift; United States; On-site; USD 150,000 - 210,000; Full time

Job summary

Airswift seeks a Computational Solutions Architect to design and deliver real-time optimization systems for industrial operations. You will translate complex engineering challenges into scalable AI-driven workflows using physics-informed models, CFD, and data-driven techniques.

You will collaborate with clients and ML engineers to define data flows, develop PoV pilots, and guide deployments from prototype to production. Strong communication and client-facing skills are essential.

Qualifications

• 5+ years designing or deploying computational systems in industrial or energy sectors.
• Strong foundation in physics-based simulation and numerical methods.
• Experience with real-time optimization and digital twins.

Responsibilities

• Lead technical design for client projects and translate challenges into workflows.
• Define data flows and integration requirements with ML engineers.
• Develop pilots and PoV initiatives to prove feasibility and value.
• Support production deployment and long-term adoption.
• Create reference architectures, templates, and documentation.
• Engage clients as trusted advisors with technical stakeholders.

Skills

Real-time optimization

Digital twins

Physics-based simulation

Machine learning

Client-facing communication

Education

MS/PhD in Engineering

MS/PhD in Applied Physics

MS/PhD in Computational Science

Job description

A leading technology innovator is seeking a Computational Solutions Architect to design and deliver advanced real-time optimization systems for industrial operations. This organization is at the cutting edge of physics-informed AI, digital twins, and predictive modeling, driving transformation across energy and other industrial sectors.

The role offers a collaborative environment where technical expertise and creative problem-solving are highly valued. You’ll work closely with clients and internal teams to turn complex engineering challenges into scalable AI-driven workflows that deliver measurable business impact.

Key Responsibilities

• Solution Design & Delivery: Lead technical design for client projects, translating engineering challenges into computational workflows that integrate physics-based models and machine learning.
• Workflow Development: Collaborate with ML engineers to define data flows, integration requirements, and ensure robust, scalable solutions.
• Proof-of-Value Projects: Develop pilots and PoV initiatives to validate technical feasibility and demonstrate business value.
• Deployment & Scale-Up: Support transition from prototype to production environments, ensuring long-term adoption.
• Best Practices: Create reference architectures, reusable templates, and documentation for future implementations.
• Client Engagement: Act as a trusted advisor, building strong relationships with technical stakeholders and decision-makers.

Core Qualifications

• Advanced degree (MS or PhD) in Engineering, Applied Physics, or Computational Science.
• 5+ years of experience designing or deploying computational or data-driven systems in industrial or energy sectors.
• Strong foundation in physics-based simulation and numerical methods (e.g., process simulation, CFD, dynamic systems).
• Familiarity with real-time optimization, digital twins, and decision support systems.
• Proven client-facing experience in technical consulting, solution delivery, or pre-sales engineering.
• Excellent communication and collaboration skills; ability to thrive in fast-paced environments.

Preferred Experience

• Background in the energy industry, particularly oil and gas.
• Exposure to startup or early-stage technology environments.