Lead AI/ML Engineer
Not just a job, but a career
Yokogawa, award winner for ‘Best Asset Monitoring Technology’ and ‘Best Digital Twin Technology’ at the HP Awards, is a leading provider of industrial automation, test and measurement, information systems and industrial services in several industries.
Our aim is to shape a better future for our planet through supporting the energy transition, (bio)technology, artificial intelligence, industrial cybersecurity, etc. We are committed to the United Nations sustainable development goals by utilizing our ability to measure and connect.
About the Team
Our 18,000 employees work in over 60 countries with one corporate mission, to "co-innovate tomorrow".We are looking for dynamic colleagues who share our passion for technology and care for our planet. In return, we offer you great career opportunities to grow yourself in a truly global culture where respect, value creation, collaboration, integrity, and gratitude are highly valued and exhibited in everything we do.
We are looking for a Lead AI/ML Engineer with deep technical expertise and proven leadership in delivering impactful solutions for the oil & gas industry. In this role, you will drive the design, development, and implementation of advanced AI/ML models, working closely with cross-functional teams to optimize operations and deliver data-driven insights in challenging industrial environments.
Job Overview
You will be part of a Project Delivery team to:
Conduct Site survey of the customer requirements and develop
Design and develop AI models that support asset performance management across various maintenance strategies, including predictive, prescriptive, and cognitive maintenance.
Implement Analytical AI techniques (supervised/unsupervised learning, reinforcement learning) for predictive maintenance, defect elimination, and asset strategy optimization.
Leverage Generative AI (Large Language Models, Deep Reinforcement Learning) to enable multi-agent systems for collaborative decision-making and autonomous goal-seeking behavior.
Use AI to analyze trends and patterns, make intelligent recommendations, and automate decision support systems.
Develop intelligent recommendations systems that improve maintenance processes, criticality assessments, and reliability analytics.
Ensure AI models are scalable and deployable within industrial platforms, integrating with PLC, DCS, SCADA, Historians, EAM, MES/MOM, SCM, and ERP systems.
Ensure compliance with ethical AI principles, particularly in terms of fairness, transparency, and bias mitigation.
Key Responsibilities
AI/ML Strategy & Solution Design
Define the AI/ML strategy and roadmap for oil & gas solutions, aligning with Yokogawa’s digital transformation objectives.
Architect end-to-end ML systems, from data ingestion and feature engineering to model deployment and monitoring.
Technical Leadership & Mentorship
Lead and mentor a multidisciplinary team of data scientists, machine learning engineers, and software developers.
Advocate best practices in model development, version control, MLOps, and engineering excellence.
Data Pipeline & Model Lifecycle Management
Oversee data gathering, cleaning, and preprocessing from diverse sources (e.g., sensors, IoT devices, DCS, SCADA systems).
Implement robust CI/CD pipelines to streamline model development, testing, deployment, and updates.
Optimization & Real-time Analytics
Develop and optimize algorithms for real-time analytics and predictive maintenance in upstream, midstream, and downstream operations.
Perform model performance tuning to ensure reliability and scalability under production environments.
Stakeholder Collaboration & Communication
Partner with domain experts, process engineers, and project managers to translate complex operational challenges into AI-driven solutions.
Present technical outcomes to both technical and non-technical audiences, highlighting business value and ROI.
Compliance & Risk Management
Ensure all AI/ML solutions comply with industry regulations, safety standards, and data governance policies.
Proactively address potential risks related to data privacy, model bias, and operational safety.
Requirements
Bachelor’s/master’s in computer science, AI, Machine Learning, or related field.
8+ years of hands-on experience in AI/ML, with at least 3 years in a senior or lead role.
Proven project delivery experience in industrial or energy sectors, with a preference for oil & gas.
Strong experience with Predictive Analytics and Prescriptive Analytics using tools like TensorFlow, PyTorch, and Keras.
Proven experience in Generative AI, RAG and vector embeddings for optimized knowledge retrieval and decision-making, and multi-agent systems for industrial applications.
Strong foundation in machine learning algorithms (supervised, unsupervised, reinforcement learning), statistical modelling, and optimization techniques.
Proficiency in handling large-scale data, time-series data, and sensor/IoT data within industrial contexts.
Expertise in cloud-based AI deployments (AWS, Azure, or Google Cloud) and edge AI for real-time decision-making.
Demonstrated knowledge of oil & gas processes (upstream, midstream, downstream), instrumentation, and control systems.
Familiarity with real-time data challenges and solutions specific to high-stakes industrial environments.
Experience with SCADA, DCS, PLCs, smart sensors, IoT platforms, and Historians.
Strong analytical, problem-solving, and communication skills, with a proven ability to work across teams.
Knowledge/ Professional Skills (Technical knowledge or skills required to perform the job)
Programming & Frameworks
Languages: Proficiency in Python is essential; exposure to C++/Java or other languages is a plus.
ML Libraries: Expert-level knowledge of TensorFlow, PyTorch, Scikit-learn; familiarity with Keras, XGBoost.
MLOps & DevOps
Experience with CI/CD pipelines, containerization (Docker), orchestration (Kubernetes), and version control (Git).
Exposure to logging, monitoring, and performance tuning tools to maintain high availability and performance of AI/ML solutions.
Cloud & Big Data
Hands-on experience with major cloud platforms (AWS, Azure, or GCP) for model deployment and data processing.
Familiarity with distributed data processing and big data technologies (Spark, Hadoop), plus knowledge of time-series databases (e.g., InfluxDB, OSIsoft PI) is an added advantage.
Data Engineering & Integration
Understanding of data ingestion workflows, ETL/ELT processes, and streaming data (Kafka, MQTT etc.).
Experience integrating AI/ML solutions into existing industrial control systems and operational dashboards.
Personal Attributes (Special personal characteristics/ interpersonal skills)
Consistently demonstrates exceptional technical skills, competence, and productivity.
Deeply passionate about transforming emerging technologies into practical, industrial solutions.
Possesses excellent communication and interpersonal abilities.
A fast learner with a strong aptitude for collaboration and teamwork.
Yokogawa is an Equal Opportunity Employer. Yokogawa wants a diverse, equitable and inclusive culture. Wewill actively recruit, develop, and promote people from a variety of backgrounds who differ in terms of experience, knowledge, thinking styles, perspective, cultural background, and socioeconomic status. Wewill not discriminate based on race, skin color, age, sex, gender identity and expression, sexual orientation, religion, belief, political opinion, nationality, ethnicity, place of origin, disability, family relations or any other circumstances. Yokogawa values differences and enables everyone to belong, contribute, succeed, and demonstrate their full potential.
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