What are the responsibilities and job description for the AI Data Engineer position at Jobs via Dice?
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Experience Bands: Mid-Level: 4-7 years, Senior/Principal: 8-12 years (technical lead, architecture)
Role Duty : As an AI Engineer, you will architect, build, deploy, and scale AI systems-covering LLM, CV, and time-series inference-across cloud and edge for drilling, production, subsurface, and HSE use cases. You'll operationalize data science models, enable real-time inference, integrate with enterprise platforms, and enforce security, governance, and reliability.
Key Responsibilities: AI Systems Engineering: Low-latency inference services, stream processing (Kafka/Spark Structured Streaming), batch pipelines, and feature stores.
LLM/GenAI Engineering: Retrieval Augmented Generation (RAG) over technical manuals, procedures, incident reports, and field notes. On-prem/virtual network safeguarded deployments; prompt engineering, grounding, evaluation harnesses. Halliburton context: knowledge assistants for operations, HSE checklists, troubleshooting flows.
Computer Vision: Defect detection, PPE compliance, corrosion/crack detection, facility inspections with edge-friendly models.
MLOps/Model Serving: CI/CD for ML, IaC, container orchestration (Docker/Kubernetes), model registries, canary/blue-green, observability (latency, throughput, model health).
Data Engineering for AI: ETL/ELT, orchestrations (Airflow/Azure Data Factory), lakehouse integration, OSDU alignment.
Security & Compliance: RBAC, VNET integration, key management, PII/HSE-sensitive data handling.
Collaboration: Partner with Data Scientists, SMEs, and product teams to land robust AI services.
Qualification:
Bachelor's Degree in Computer Engineering, or commensurate qualification from an accredited university.
Experience Bands: Mid-Level: 4-7 years, Senior/Principal: 8-12 years (technical lead, architecture)
Role Duty : As an AI Engineer, you will architect, build, deploy, and scale AI systems-covering LLM, CV, and time-series inference-across cloud and edge for drilling, production, subsurface, and HSE use cases. You'll operationalize data science models, enable real-time inference, integrate with enterprise platforms, and enforce security, governance, and reliability.
Key Responsibilities: AI Systems Engineering: Low-latency inference services, stream processing (Kafka/Spark Structured Streaming), batch pipelines, and feature stores.
LLM/GenAI Engineering: Retrieval Augmented Generation (RAG) over technical manuals, procedures, incident reports, and field notes. On-prem/virtual network safeguarded deployments; prompt engineering, grounding, evaluation harnesses. Halliburton context: knowledge assistants for operations, HSE checklists, troubleshooting flows.
Computer Vision: Defect detection, PPE compliance, corrosion/crack detection, facility inspections with edge-friendly models.
MLOps/Model Serving: CI/CD for ML, IaC, container orchestration (Docker/Kubernetes), model registries, canary/blue-green, observability (latency, throughput, model health).
Data Engineering for AI: ETL/ELT, orchestrations (Airflow/Azure Data Factory), lakehouse integration, OSDU alignment.
Security & Compliance: RBAC, VNET integration, key management, PII/HSE-sensitive data handling.
Collaboration: Partner with Data Scientists, SMEs, and product teams to land robust AI services.
Qualification:
Bachelor's Degree in Computer Engineering, or commensurate qualification from an accredited university.