Principal AI/ML Engineer

• 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 the 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 (e.g., vibration, acoustic emission, SCADA/Historian data).
• 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, setting technical standards for analytics engineering and acting as a technical authority across cross-functional project teams.
• Develop, tune, and productionize machine learning models for quality inspection, anomaly detection, and process optimization.

• 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.

• Bachelor’s or Master’s degree in Engineering (Mechanical, Structural, Electrical, or related discipline); an 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).

• 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.

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

Diversity & Inclusion Statement: We are committed to building a diverse and inclusive team and encourage candidates from all backgrounds to apply.

SymphonyAI is building the leading enterprise AI SaaS company for digital transformation across the most critical and resilient growth industries, including retail, consumer packaged goods, financial crime prevention, manufacturing, media, and IT service management.

Since its founding in 2017, SymphonyAI today serves 1500+ Enterprise customers globally and has grown to 3,000 talented leaders, data scientists, and other professionals across over 30 countries.