Senior Analytics & AI Engineer

TGS; Fredrikstad; In loco; NOK 1.100.000 - 1.600.000; Tempo pieno

Descrizione del lavoro

TGS provides scientific data and analytics to energy companies. This role at TGS Prediktor focuses on leveraging data, analytics, and AI to improve performance of energy assets.

You will build end-to-end data and ML pipelines, explore LLMs and AI agents, and ensure robust, secure production capabilities across teams. You will work across research, engineering, product, and domain experts to turn customer problems into scalable product features.

Competenze

• 7–8+ years of relevant professional experience across data engineering, analytics, ML and production software environments.
• Experience taking ML or analytical solutions into production beyond notebooks/PoCs.
• Proven ability to design robust data and ML pipelines and apply statistics to real-world problems.

Mansioni

• Develop analytics, ML and AI capabilities from PoC through to production.
• Design and build robust data and ML pipelines supporting analytics and AI apps.
• Apply MLOps/ModelOps and DevOps practices to ensure reproducibility and security.

Conoscenze

Python

SQL

Data engineering

Statistics

ML production

DevOps practices

LLMs

AI agents

Cross-disciplinary

Formazione

Degree in CS/Math/Engineering

Strumenti

CI/CD

Containers

Cloud platforms

ModelOps

Descrizione del lavoro

TGS provides scientific data and intelligence to companies active in the energy sector. In addition to a global, extensive and diverse energy data library, TGS offers specialized services such as advanced processing and analytics alongside cloud-based data applications and solutions. TGS Prediktor is a leading asset management and real-time data management solutions provider to renewable and energy asset owners.

About The Role

At TGS Prediktor, we use data, analytics, and AI to help our customers understand and improve the performance of their energy and industrial assets. We are looking for a Senior Analytics & AI Engineer who combines strong analytical and machine-learning expertise, with the engineering skills needed to bring ideas into production. You will work across the full lifecycle, from exploring data and developing models and proofs of concept to building the data, ML and DevSecOps pipelines required to turn them into scalable, reliable product capabilities. The role spans both classical analytics and machine learning and modern AI technologies such as LLMs, RAG, and AI agents. You will help us choose the right approach for the problem and ensure that what we build can be operated successfully in production.

What You Will Do

• Develop analytics, ML and AI capabilities from idea and PoC through to production
• Design and build robust data and ML pipelines supporting analytics and AI applications
• Develop and apply statistical, machine-learning and optimization methods to real-world industrial and energy problems
• Explore and apply LLMs, RAG, and agentic AI where they create meaningful product value
• Build automation for testing, deployment, monitoring and continuous improvement of models and AI capabilities
• Apply MLOps/ModelOps and DevOps practices to make solutions reproducible, observable, maintainable and secure
• Work closely with product teams, software engineers, and domain experts to turn customer problems into scalable product capabilities
• Contribute to architecture and technology decisions across analytics, AI and data engineering

What We Are Looking For

• You are an experienced engineer or applied scientist who enjoys working across disciplines rather than within a narrow specialization.
• You typically bring 7–8+ years of relevant professional experience across data engineering, analytics, machine learning and production software environments.
• You are self-driven and comfortable taking ownership of complex problems, proactively exploring solutions, and driving them from initial idea and experimentation through to production.

You Also Bring

• Strong experience with Python, SQL, and modern software engineering practices
• Solid experience in data engineering, including data modelling, transformation, pipelines, and data quality
• Strong understanding of statistics, analytics, and classical machine learning
• Experience taking ML or analytical solutions beyond notebooks and PoCs into production
• Experience with cloud platforms, CI/CD, containers, and DevSecOps practices
• Understanding of MLOps/ModelOps, including model evaluation, versioning, deployment, monitoring, and lifecycle management
• Practical knowledge of modern LLM technologies, including embeddings, RAG, tool use, and AI agents
• Good judgement about when to use traditional analytics/ML versus generative AI
• The ability to work effectively across research, engineering, product, and domain disciplines
• A relevant degree in computer science, mathematics, engineering or a related field is valuable, but equivalent practical experience matters just as much.

Please note that you must have the legal right to work in Norway.