Lead AI/ML Engineer (m/f/d) at synvert International(Recruiting)

Abu Dhabi, Abu Dhabi Emirate, United Arab Emirates -

Full Time

Start Date

Immediate

Expiry Date

09 Aug, 26

Salary

0.0

Posted On

11 May, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Spark, Databricks, XGBoost, MLOps, Microsoft Azure, CI/CD, Time-series Analysis, Deep Learning, Optimization, Operation Research, Data Engineering, Stakeholder Management, Predictive Maintenance, Production Engineering, Model Governance

Industry

IT Services and IT Consulting

Description

About synvert a GlobalLogic company 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. What do we expect from you? Implementation 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). Business and triage 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. Strongly preferred Domain 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. Enterprise context 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. What you can expect Agile Company Culture And The Best Team 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). Continuous development 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. Working Model Abu Dhabi - In office Other Benefits 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 Hiring Process

Responsibilities

Lead the end-to-end implementation of AI/ML solutions from problem framing to deployment, focusing on industrial and energy use cases. Act as a technical co-owner of the AI/ML portfolio, translating business problems into technical specifications for operational decision-making.