Experimentation and Prototype Development: Design, develop, and assess data-driven algorithms for various tasks (regression, classification, segmentation, etc.) using cutting-edge AI techniques.
Prototype and evaluate LLM-based systems and multimodal models for tasks such as document understanding, knowledge extraction, and workflow automation.
End-to-End Implementation: Hands-on implementation of AI models, from data preparation and cleaning to model deployment and maintenance in production.
Integration of Pipelines: Contribute to the solution design and collaborate with teams to integrate AI-enabled software products for the oil & gas industry.
Monitoring Model Performance: Evaluate and monitor AI solutions to ensure they align with project objectives, addressing data quality issues and continuously improving existing solutions.
Qualifications
Requirements – Experience
At least 4 years of experience demonstrating depth and breadth in Computer Vision projects (classification, detection, segmentation) with CV approaches.
Demonstrated experience with state-of-the-art machine-learning and/or deep-learning technologies.
Demonstrated experience in developing, deploying and scaling end to end ML pipelines in industrial context.
Hands-on experience building and deploying LLM applications (e.g., GPT, Llama, Falcon, Claude), including fine-tuning, RAG systems, domain adaptation, or multimodal extensions.
Demonstrated experience designing and implementing agentic AI systems—task-oriented agents, workflow orchestrators, tool-using agents, or autonomous reasoning frameworks.
Experience in Geoscience applications to the Oil & Gas sector is a plus.
Requirements – Key Skills
Strong foundation in applied mathematics and statistics.
Proficiency in machine learning and deep learning techniques.
Advanced Python programming skills for AI development.
Extensive experience with classic CV tools (OpenCV), deep learning frameworks (PyTorch, TensorFlow), and popular ML libraries (Scikit-learn).
Comprehensive knowledge and practical application of diverse ML algorithms and DL architectures.
Proficiency in essential development tools like PyCharm, Jupyter, ClearML, Git, and Docker.
Strong knowledge of LLM frameworks and tooling (LangChain, LlamaIndex, Hugging Face, OpenAI/Anthropic APIs).
Proficiency in prompt engineering, evaluation frameworks, and structured prompt design.
Familiarity with agent frameworks (LangGraph, AutoGen, CrewAI, or custom agent architectures).
Autonomy in problem-solving and project execution, demonstrating the ability to work independently and in a team framework.
Excellent communication skills for conveying technical concepts effectively.
Educational Requirements
Master's degree or Ph.D. in Computer Science, Applied Mathematics, Statistics, or any AI-related field.
