Artificial Intelligence Engineer

Job Summary

We are looking for an experienced AI / Machine Learning Engineer with strong expertise in building and deploying AI solutions in production environments. The ideal candidate should have hands-on experience with Computational Knowledge Graphs, strong mathematical foundations, and practical exposure to machine learning, optimization techniques, and scalable system design. Experience working with Oil & Gas domain data and business processes is highly preferred.

Key Responsibilities

• Design, develop, deploy, and maintain AI/ML applications for production-scale environments.
• Build and optimize Computational Knowledge Graph solutions for intelligent data modeling and decision support.
• Develop machine learning models for prediction, classification, optimization, and analytics use cases.
• Implement scalable AI architectures and integrate them with enterprise platforms.
• Work with structured and unstructured datasets to derive business insights.
• Design and optimize algorithms using mathematical and statistical techniques.
• Develop APIs and backend services for AI model integration.
• Collaborate with cross-functional teams including data engineers, software developers, and business stakeholders.
• Apply AI solutions to solve business problems in the Oil & Gas domain.
• Monitor model performance and continuously improve system reliability and efficiency.
• Follow MLOps and software engineering best practices for deployment and lifecycle management.

Required Skills & Qualifications

Technical Skills

• Strong proficiency in Python (mandatory)
• Good programming knowledge in Java, C++, TypeScript/JavaScript
• Strong mathematical foundations including:
• Linear Algebra
• Probability & Statistics
• Optimization Techniques
• Calculus (preferred)
• Solid understanding of:
• Machine Learning algorithms
• Deep Learning concepts
• Feature Engineering
• Model Evaluation & Optimization
• System Design principles
• Experience with Computational Knowledge Graphs / Graph-based AI systems
• Exposure to AI deployment and production-grade ML systems
• Experience with cloud platforms and scalable architecture is preferred
• Understanding of MLOps concepts and model lifecycle management