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Data Analytics and GenAI Architect
9 hours ago
Job DescriptionRole Title: Data Analytics and GenAI ArchitectRequired Technical Skills: GenAI, ML, LLM, Data Analytics, Data Engineering (Spark, Delta Lake), BI Tools (Power BI, Tableau), Cloud Data Platforms (AWS/Azure/GCP).Location: Calgary, CanadaWork Style: RemoteRole Description:SN Responsibility of / Expectations from the Role1 Design and development: Create automated systems, frameworks, and scripts for machinery or software applications. This includes designing the system architecture and developing solutions for automation problems.2 Implementation and integration: Deploy automation solutions and integrate them with existing systems and other tools. This can involve working with tools like Ansible, Kibana, or CI/CD pipelines.3 Maintenance and troubleshooting: Troubleshoot and resolve automation errors and bugs. They are also responsible for maintaining and upgrading existing automation technology and improving system performance.4 Collaboration and communication: Work with cross-functional teams, such as developers and QA professionals, to identify automation opportunities and define requirements. They also provide technical support and training to colleagues.5 Testing and quality assurance: Develop test automation frameworks and scripts to validate product functionality and performance. They analyze test results, report defects, and ensure the quality of the automated systems.6 Define data architecture strategy for analytics and AI integration7 Build data pipelines and analytics dashboards for operational insights8 Collaborate with business teams to translate analytics needs into AI-driven solutions.Must Have:1) 6 plus years professional software engineering, including 3+ year building and shipping GenAI or LLM-driven products.2) Architect, develop, and deploy GenAI services that ingest SCADA, PI System, WITSML, and reservoir data to deliver insights for drilling optimization, equipment reliability, and emissions reduction.3) Fine-tune and evaluate large language and vision models on domain-specific corpora (well files, P&IDs, procedures, regulatory filings).4) Build secure, scalable APIs and micro-services in Python/TypeScript; containerize with Docker and serve on Kubernetes/EKS or OpenShift.5) Implement retrieval-augmented generation pipelines with vector databases (e.g., OpenSearch, Milvus) to enable fast technical-document Q&A for engineers and field operators.6) Optimize model inference on GPUs/accelerators using DeepSpeed, TensorRT, or vLLM; benchmark latency, throughput, and cost.7) Embed GenAI capabilities into web, mobile, and edge applications used at well sites, plants, and control centres, ensuring robust observability and rollback.8) Uphold best-practice software engineering: CI/CD (GitHub Actions, ArgoCD), automated testing, IaC (Terraform/Pulumi), and secure coding aligned with AER regulatory requirements.9) Monitor academic and industry advances (RLHF, RAG, agentic workflows) and champion pragmatic adoption in oil & gas contexts.10) Design and implement data pipelines and analytics frameworks for large-scale structured/unstructured data.11) Define data governance and quality standards for analytics and AI models.12) Integrate GenAI with BI dashboards and predictive analytics workflows.13) Ensure data security and compliance across analytics and AI systems.14) Good communication, analytical, presentation and documentation skills.Good to Have:1) Experience integrating GenAI with historian data, geoscience interpretation tools (Petrel, Kingdom), or maintenance systems (SAP PM, Maximo).2) Contributions to open-source GenAI/ML projects or published papers.3) Familiarity with LangChain, LlamaIndex, or similar orchestration frameworks.4) Knowledge of energy-sector regulations (AER, CAPP) impacting data use and model governance.5) Background in real-time analytics for production optimization or flare/emissions monitoring.6) Experience with data visualization tools (Power BI, Tableau).7) Knowledge of data warehousing and lakehouse architectures.8) Familiarity with ETL tools and data orchestration frameworks (Airflow, dbt).Technical Skills:1) Advanced Python (FastAPI, PyTorch, Hugging Face); plus proficiency in TypeScript/Node.js, Go, or Java.2) Production experience on AWS or Azure with GPU workloads (SageMaker, Bedrock, Azure ML).3) Working knowledge of oil & gas data formats and systems (e.g., LAS, WITSML, PI AF, OSDU) or proven ability to learn quickly.4) Strong grasp of algorithms, distributed systems, and data engineering (Spark, Kafka, Delta Lake).Commitment to responsible AI, data governance, and cybersecurity best practices for critical infrastructure5) SQL and data modeling for analytics.6) Data pipeline orchestration (Airflow, dbt).7) BI tools (Power BI, Tableau).8) Cloud-native data services (AWS Glue, Azure Synapse, BigQuery).Soft Skills:1) Problem-Solving:a. Analytical thinking and the ability to troubleshoot complex issues.b. Creativity in designing innovative solutions.2) Communication:a. Effective communication with technical and non-technical stakeholders.b. Ability to document and present architectural designs and solutions clearly.3) Continuous Learning:a. Staying updated with the latest Azure features, best practices, and industry trends.b. Pursuing relevant certifications and training programs.RequirementsAndroid and iOS
