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Exciting Opening for Principal AI/ML Architect - Industrial AI & Asset Intelligence.
Dear Folks,
We have an exciting opportunity for the above role @ TAO Digital Solutions (www.taodigitalsolutions.com)
Interested candidates please forward your updated resume to the following email ID (mohan.kaliappan@taodigitalsolutions.com) ASAP.
Experience: 12 to 18+ years overall. Candidate should have min 6+ years in AI/ML; 3+ years leading production AI architecture in asset-intensive environments
Pref Industry: Telecommunications; Energy / Oil & Gas; Automotive / Manufacturing; Aerospace / Aviation
Preferred platform / tool exposure:
AI/ML: Python, SQL, PyTorch/TensorFlow, scikit-learn, XGBoost/LightGBM, MLflow, Dataiku, Jupyter, model serving APIs.
GenAI: Azure OpenAI / AI Foundry, AWS Bedrock, Google Vertex AI; LangGraph/LangChain/Semantic Kernel; vector databases such as Pinecone, Weaviate, Milvus, pgvector or OpenSearch; graph technology such as Neo4j is a plus.
Industrial / edge: Kafka or equivalent event streaming; MQTT, OPC-UA; Docker/Kubernetes; AWS IoT Core/SiteWise/Greengrass/TwinMaker, Azure IoT Hub/IoT Edge/Digital Twins, or equivalent IIoT platforms.
Operational systems: IBM Maximo, SAP PM/EAM, ServiceNow, AVEVA PI/OSIsoft historian, SCADA/DCS, MES/MOM; telecom OSS/BSS/NMS/service-assurance platforms; PLM/MRO systems where applicable.
Observability / visualization: Grafana, Power BI/Tableau, cloud monitoring stacks; AI/model observability platforms are desirable.
Experience & qualifications
12+ years in software, data science, ML engineering, industrial analytics or related technology roles, with clear progression into architecture/technical leadership.
6+ years building and deploying AI/ML solutions; at least 3 years owning architecture or technical leadership for production-grade AI programs.
Demonstrable work with high-volume time-series, telemetry, event or machine-generated data and at least one asset-intensive industry.
Evidence of taking AI from discovery/PoC through production and operationsnot only experimentation or notebooks.
Strong architecture and design communication: can produce target-state architectures, integration patterns, ADRs, non-functional requirements, risk/assumption logs and implementation roadmaps.
Bachelors or Master’s degree in Computer Science, AI/ML, Data Science, Electrical/Mechanical/Industrial/Aerospace Engineering, Applied Mathematics or related field. Advanced degree is preferred but equivalent industrial experience is acceptable.
Strong stakeholder skills with engineering leaders, maintenance/reliability teams, operations, IT/OT, cybersecurity, product management and executive audiences.
Work arrangement
Hybrid
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