[Hiring] Principal AI/ML Engineer @SymphonyAI

Role Description

SymphonyAI is looking for a Principal AI/ML Engineer to lead the design and delivery of production-grade machine learning and physics-informed analytics solutions for industrial customers. This is a hands-on technical leadership role for someone who has spent a career at the intersection of data-driven modeling and first-principles engineering, and who can take agentic AI, anomaly detection, and predictive automation capabilities from prototype to plant-floor deployment. The role sits within the Industrial AI vertical and partners closely with product, R&D, and customer engineering teams across Oil & Gas, Chemical, and manufacturing domains.

• Architect and build machine learning and physics-model-based analytics systems for industrial use cases such as anomaly detection, root-cause analysis, predictive maintenance, and asset health monitoring.
• Lead design and delivery of agentic AI and contextual AI workflows that combine domain knowledge with data-driven models for autonomous fault diagnosis and predictive automation.
• Own the technical roadmap for DataOps pipelines that ingest, process, and contextualize high-volume industrial sensor and time-series data (vibration, acoustic emission, SCADA/Historian data).
• Apply signal processing and vibration/acoustic analysis techniques to build health-monitoring and condition-based-monitoring models for rotating and industrial equipment.
• Develop, tune, and productionize machine learning models (including image analytics and computer vision where relevant) for quality inspection, anomaly detection, and process optimization.
• Design and scale Python-based analytics applications and internal tools using frameworks such as Flask, Dash, Bokeh, or Plotly for engineering and customer-facing use.
• Drive model deployment and MLOps practices across cloud and on-prem environments (e.g., GCP, GPU/DGX infrastructure), including performance tuning and code optimization.
• Mentor senior and mid-level engineers, set technical standards for analytics engineering, and act as a technical authority across cross-functional project teams.
• Partner with product management and customer-facing teams to translate industrial domain requirements (e.g., compressor, turbine, or asset diagnostics) into scalable AI product features.
• Represent the technical roadmap in front of internal leadership and, where required, key customers or partners.

Qualifications

• Bachelor's or Master's degree in Engineering (Mechanical, Structural, Electrical, or related discipline); advanced degree from a top-tier institute preferred.
• 12+ years of experience in analytics/ML engineering, with a demonstrated track record spanning both data-driven and physics/first-principles modeling approaches.
• Deep experience in industrial R&D domains such as asset health monitoring, diagnostics and prognostics, or additive manufacturing.
• Strong hands-on programming skills in Python, including production-grade software engineering practices (testing, performance tuning, deployment).
• Proven experience applying machine learning algorithms to real-world industrial datasets — including signal/vibration data, image data, or process sensor data.
• Experience building and deploying models on cloud or GPU infrastructure (e.g., GCP, DGX, or equivalent).
• Exposure to Generative AI and Agentic AI frameworks and contextual/industrial AI platforms.
• Experience with building real-world RAG-based systems leveraging frontier LLMs or fine-tuned open-source LLM/SLMs.
• Demonstrated project or technical leadership experience, including managing delivery for enterprise or global R&D stakeholders (e.g., onsite technical lead roles).

Preferred Qualification

• Experience with additive manufacturing analytics, part quality inspection, or process-parameter optimization.
• Familiarity with web-based analytics tooling (Flask, Dash, Bokeh, Plotly) for building internal or customer-facing engineering applications.
• Relevant industrial AI or DataOps certification (e.g., IRIS Foundry Technical AI Professional Certification or equivalent).
• Experience mentoring engineers and setting technical direction as a principal-level individual contributor.

Benefits

• Industrial anomaly detection and predictive models are deployed reliably in production with measurable uptime or maintenance-cost impact for customers.
• Agentic and contextual AI workflows reduce manual root-cause analysis time for plant engineering teams.
• The engineering team has clear technical standards, reusable DataOps infrastructure, and a strong bench of well-mentored engineers.