Job Description & How to Apply Below
Dear Folks,
We have an exciting opportunity for the above role @ TAO Digital Solutions ()
Interested candidates please forward your updated resume to the following 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:
Core engineering :
Python, SQL, PySpark/Scala, Spark, Kafka/Confluent, dbt, Airflow/Dagster, REST/GraphQL APIs, Git, Docker/Kubernetes, Terraform or equivalent IaC.
Lakehouse / analytics :
Databricks + Delta Lake, Snowflake, Big Query, Redshift, Synapse/Fabric;
Apache Iceberg/Hudi familiarity is valuable.
Cloud : strong depth in at least one of AWS, Azure or GCP and architectural familiarity with a second. Examples: S3/Glue/EMR/Kinesis/MSK; ADLS/Data Factory/Event Hubs/Fabric; GCS/Dataflow/Pub/Sub/Big Query.
Industrial / edge : AWS IoT Core/Site Wise/Green grass/Twin Maker, Azure IoT Hub/IoT Edge/Digital Twins, MQTT brokers, OPC-UA gateways; familiarity with AVEVA PI/OSIsoft historians, SCADA/DCS and MES/MOM.
Governance :
Databricks Unity Catalog, Microsoft Purview, AWS Glue/Lake Formation, Collibra/Alation, data-quality/observability tooling such as Great Expectations, Soda, Monte Carlo or equivalent.
Operational applications : IBM Maximo, SAP PM/EAM, Service Now; telecom OSS/BSS/NMS; automotive PLM/MES; aerospace MRO/engineering systems are desirable depending on domain.
Experience & qualifications
12+ years in data engineering, data platforms, software/platform engineering or architecture, with 3+ years owning senior architecture/technical leadership responsibilities.
Proven experience with high-volume, high-velocity telemetry, IoT/IIoT, event, log or time-series data—not only traditional BI/warehouse workloads.
Demonstrated architecture and delivery across at least one asset-intensive industry, with working understanding of maintenance/reliability or operational workflows.
Evidence of building platforms that are used in production by data science/AI and operations teams, including clear SLAs, observability, governance and cost controls.
Strong knowledge of security and networking considerations for hybrid edge-to-cloud and OT/IT integrations.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Electrical/Computer/Industrial Engineering or related discipline; equivalent industry experience acceptable.
Ability to communicate with plant/site/network engineers, reliability teams, data scientists, cybersecurity, enterprise architects and executive stakeholders.
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