Senior Data & AI Engineer - Oil & Gas

Job summary

Design and implement end-to-end AI/ML solutions and scalable data pipelines for industrial operational data. Collaborate with SMEs to translate complex engineering requirements into traceable and auditable technical workflows.

Job details

We are looking for a Senior AI/ML Engineers to join our team, that sit at the intersection of software engineering, data engineering, and applied AI. This role is ideal for someone who enjoys building end-to-end solutions - from data pipelines and backend systems to AI-powered applications - and wants to work on real-world industrial use cases that comes from an Engineering background, and has experience in Oil & Gas. In this position, you’ll contribute to a variety of impactful client projects, including: Building AI-powered anomaly detection systems for operational and industrial data Modernizing asset management workflows Developing natural language interfaces for querying enterprise and operational data Designing and implementing AI agents and copilots for business users Creating scalable data pipelines and ML workflows in cloud environments Enabling real-time and batch data processing for analytics and AI use cases Working through complex and challenging data conditions, including incremental processing, late-arriving or changing records, complex business and engineering rules, and reconciliation of results across processing runs The ideal candidate combines strong data engineering experience with an engineering or applied-science background. Direct experience industrial time-series data, scientific measurements, telemetry, financial reconciliation, or other datasets where accuracy, traceability, and incremental recalculation are critical. Key Responsibilities Design, build, and optimize pipelines for sensor and related operational data. Develop complex transformation and calculation logic based on engineering requirements. Implement robust incremental-processing patterns for high-volume and continuously changing datasets. Design, build, and deploy end-to-end AI/ML solutions in production environments Develop robust backend systems and APIs to support AI-driven applications Build and maintain data pipelines and feature engineering workflows Implement and operationalize machine learning models (training, deployment, monitoring) Work with modern AI tooling (LLMs, agents, orchestration frameworks) Collaborate with clients to translate business problems into technical solutions Contribute to architecture decisions and best practices across projects Mentor client team members and contribute to internal capability building Work directly with engineering and operational SMEs to understand physical processes and translate their knowledge into technical requirements. Make engineering calculations and data transformations explainable, traceable, testable, and auditable. Document data lineage, calculation logic, assumptions, dependencies, and exception-handling rules. Ideal Background Senior-level experience designing and developing production data pipelines with Azure Databricks including strong experience with complex SQL, Python, Spark, or comparable data-processing technologies. Demonstrated experience with incremental processing, change detection, reconciliation, and idempotent pipeline design. Experience handling time-series, telemetry, sensor, operational, scientific, or industrial data. Ability to work through ambiguous requirements with highly specialized SMEs. Strong analytical and investigative skills, with the patience to work through detailed logic and difficult data-quality problems. Degree or professional background in petroleum, reservoir, chemical, mechanical, geological, geophysical, or another relevant engineering or applied-science discipline is strongly preferred. Experience in upstream oil and gas, thermal operations, SAGD, well surveillance, production engineering, or subsurface data would be a significant asset. Hands-on experience with AI/ML workflows and model deployment. Modern developer tooling (Cursor, AI-assisted development, Langraph, "vibe coding"). This role presents an exciting opportunity to work on practical, high-impact AI use cases - not just prototypes, shape how AI is applied to client environments, and change the game on traditional processes and platforms. Come join a growin

What you’ll do

Design and implement end-to-end AI/ML solutions and scalable data pipelines for industrial operational data. Collaborate with SMEs to translate complex engineering requirements into traceable and auditable technical workflows.

Requirements

Requires senior-level experience with Azure Databricks, Python, and Spark, specifically handling industrial time-series data. A degree in a relevant engineering or applied-science discipline is strongly preferred.

Listed skills

• SQL · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Azure Databricks
• Python
• Spark
• SQL
• AI/ML Engineering
• Data Pipeline Design
• LLMs
• Time-Series Data
• Feature Engineering
• API Development
• Langraph
• Incremental Processing

Job areas

• Data & Analytics
• Engineering
• Energy
• Technology
• Consulting