Lead AI ML Engineer M F D

About Us

We are a specialist consulting firm delivering services and solutions in “Everything Data” – Business Intelligence, Advanced Analytics, Big Data & Cloud, and Web & Mobile Applications. We are part of a strategic alliance with Synvert, a group of six successful full-service Data & Analytics (D&A) consulting firms, with a clear goal to become one of EMEA’s largest D&A consulting companies.

Our services are based on the latest market-leading enterprise technology platforms, and delivered by a dynamic team of expert consultants. Our strength lies in our ability to efficiently deliver customer insight and value, gained through our decades of experience with real-world challenges.

As Lead AI/ML Engineer, you will work directly with business leadership to translate a backlog of use cases into delivered value. You will operate as both builder and portfolio co-owner, shipping production-grade ML solutions where appropriate, and integrating parent-group AI platforms where leverage is highest.

Your Tasks

• End-to-end implementation: Take selected use cases from problem framing → data pipeline → model → deployment → monitoring. Initial focus areas likely include artificial-lift predictive maintenance, well-control optimization, and production-system surveillance. Where appropriate, you will leverage existing group AI platforms or vendor solutions rather than building from scratch.
• AI/ML use-case portfolio: technical co-owner. The backlog is co-owned with business leadership. You contribute the feasibility, effort, timeline, and technical-risk view on each use case, propose sequencing, and own the technical defense of what is in or out of scope. The roadmap is reviewed with business leadership on a defined cadence (e.g., quarterly).
• Stakeholder translation: Partner with subsurface, production, and operations teams. Translate business problems into technical specs and translate technical results into operational decisions.
  
  

• 8+ years building and shipping production ML & AI systems in industrial, energy, manufacturing, or process domains.
• Demonstrable experience in Optimization & Operation Research problems
• Hands-on across the ML stack: Python; data engineering with Spark/Databricks or equivalent; classical ML (XGBoost, ensemble methods); time-series and deep learning where the problem warrants; MLOps fundamentals (CI/CD for models, monitoring, retraining).
• Track record of taking ML from 0 → 1, with at least one example of a solution you personally deployed running in production beyond pilot.
• Cloud platform fluency on a major cloud (Microsoft Azure preferred).
  
  

• Demonstrated ability to contribute feasibility and risk views to a shared portfolio. Ability to say no with reasoning and reset expectations on technical infeasibility.
• Strong communication with non-technical business stakeholders. Comfortable in a room with operations leadership and reservoir/production engineers.
• Experience in a founding or first-hire context. Building solutions from scratch and scaling them.
  
  

• Working knowledge of upstream oil and gas: production engineering, artificial lift (ESP, gas lift), well surveillance, basic reservoir behavior, multiphase flow.
• Familiarity with industrial sensor data and predictive maintenance patterns.
• Exposure to subsurface/seismic data is a plus but not required.
  
  

• Experience operating within large-enterprise digital governance frameworks (architecture review, security, change management, vendor management).
• Familiarity with enterprise AI/ML operating models: model lifecycle governance, ownership boundaries, audit trails.
• Prior exposure to GCC NOC/IOC operating environments is a plus.
  
  

• Join our international, ambitious, and collaborative teams that value ownership and excellence.
• Structured onboarding for smooth integration.
• Company-wide events (Christmas, Spring, Summer) and regular after works.
• Community initiatives and team-driven activities (Football team, Gaming channel).
  
  

• Work on challenging projects that accelerate your professional growth.
• Structured mentoring, training, and certification programs.
• Use of agile frameworks combined with established planning and delivery standards.
• Talent Cycle: regular performance reviews, continuous feedback, and clear career paths.
  
  

• Abu Dhabi - In office
  
  

• Flexible benefits plan: health insurance, transport, childcare vouchers.
• Remuneration is structured according to benchmarks and evolving market practices, ensuring internal consistency, external competitiveness, and alignment with business performance and professional progression.
• 25 vacation days