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Introduction Overview: SymphonyAI is a global leader in AI-powered solutions that transform industries and drive business growth through cutting-edge artificial intelligence and machine learning technologies. We empower organizations across retail, CPG, financial services, manufacturing, media, Enterprise IT, and the public sector to harness data-driven insights. Our applications deliver transformative business value by combining unrivaled AI technology, vertical expertise, and industry-specific data. SymphonyAI is one of the largest and fastest-growing AI portfolios committed to building a "World Class Engineering Team" with a high-performance culture. SymphonyAI seeks 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 hands-on technical leadership role is ideal for someone who has a career at the intersection of data-driven modeling and first-principles engineering. You will take agentic AI, anomaly detection, and predictive automation capabilities from prototype to plant-floor deployment. This role partners closely with product, R&D, and customer engineering teams across Oil & Gas, Chemical, and manufacturing domains. Job Description Key Responsibilities: Machine Learning & Analytics Development 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. DataOps & Model Deployment 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. Engineering & Mentor Leadership 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. Collaboration & Technical Representation 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. About You: 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). Preferred Qualifications: 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. What Success Looks Like: 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. About Us 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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.