Introduction:
About SymphonyAI
SymphonyAI is at the forefront of innovation, leveraging cutting-edge artificial intelligence and machine learning technologies to transform industries and drive business growth. As a global leader in AI-powered solutions, we empower organizations to harness the full potential of data-driven insights. SymphonyAI enterprise applications rapidly deliver transformative business value across retail, CPG, financial services, manufacturing, media, Enterprise IT, and the public sector. SymphonyAI combines unrivalled AI technology, vertical expertise, and industry-specific data and insights into applications that drive the highest value for customers. We are one of the largest and fastest growing AI portfolios, on a mission to build a “World Class Engineering Team” with a high-performance culture.
Job Description:
Role Summary
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.
About the role
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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.
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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.
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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).
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Apply signal processing and vibration/acoustic analysis techniques to build health-monitoring and condition-based-monitoring models for rotating and industrial equipment.
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Develop, tune, and productionize machine learning models (including image analytics and computer vision where relevant) for quality inspection, anomaly detection, and process optimization.
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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.
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Drive model deployment and MLOps practices across cloud and on-prem environments (e.g., GCP, GPU/DGX infrastructure), including performance tuning and code optimization.
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Mentor senior and mid-level engineers, set technical standards for analytics engineering, and act as a technical authority across cross-functional project teams.
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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.
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Represent the technical roadmap in front of internal leadership and, where required, key customers or partners.
About you
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Bachelor's or Master's degree in Engineering (Mechanical, Structural, Electrical, or related discipline); advanced degree from a top-tier institute preferred.
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12+ years of experience in analytics/ML engineering, with a demonstrated track record spanning both data-driven and physics/first-principles modeling approaches.
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Deep experience in industrial R&D domains such as asset health monitoring, diagnostics and prognostics, or additive manufacturing.
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Strong hands-on programming skills in Python, including production-grade software engineering practices (testing, performance tuning, deployment).
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Proven experience applying machine learning algorithms to real-world industrial datasets — including signal/vibration data, image data, or process sensor data.
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Experience building and deploying models on cloud or GPU infrastructure (e.g., GCP, DGX, or equivalent).
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Exposure to Generative AI and Agentic AI frameworks and contextual/industrial AI platforms.
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Experience with building real-world RAG-based systems leveraging frontier LLMs or fine-tuned open-source LLM/SLMs
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Demonstrated project or technical leadership experience, including managing delivery for enterprise or global R&D stakeholders (e.g., onsite technical lead roles).
Preferred Qualification
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Experience with additive manufacturing analytics, part quality inspection, or process-parameter optimization.
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Familiarity with web-based analytics tooling (Flask, Dash, Bokeh, Plotly) for building internal or customer-facing engineering applications.
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Relevant industrial AI or DataOps certification (e.g., IRIS Foundry Technical AI Professional Certification or equivalent).
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Experience mentoring engineers and setting technical direction as a principal-level individual contributor.
About Us:
What Success Looks Like
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Industrial anomaly detection and predictive models are deployed reliably in production with measurable uptime or maintenance-cost impact for customers.
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Agentic and contextual AI workflows reduce manual root-cause analysis time for plant engineering teams.
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The engineering team has clear technical standards, reusable DataOps infrastructure, and a strong bench of well-mentored engineers.
