Principal AI/ML Architect

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 ([hidden]

Interested candidates please forward your updated resume to the following email ID (hidden_email) 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 Masters 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. .