{"id":3537257,"url":"https://alion.io/job/g42-senior-software-engineer","title":"Senior Software Engineer","company":{"id":190,"name":"G42","domain":"g42.ai","url":"https://alion.io/company/g42","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Phenom","truth_index":{"grade":"A","score":94,"open_postings":7,"ghost_share":0,"stale_share":0.143,"repost_share":0.143,"time_to_fill_p50_days":24,"computed_at":"2026-11-04T06:34:25Z"}},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Abu Dhabi, United Arab Emirates"],"countries":["AE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CI/CD","optional":false},{"name":"DNP3","optional":false},{"name":"Docker","optional":false},{"name":"Git","optional":false},{"name":"Linux","optional":false},{"name":"MQTT","optional":false},{"name":"OPC UA","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"ROS","optional":true}],"status":"closed","first_seen_at":"2026-10-22T06:59:34Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-11-03T22:56:49Z","board_verified":false,"closed_at":"2026-11-03T22:56:49Z","days_open":12,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":12},"description":"About the Company:\nAIQ is an Abu Dhabi-based technology company that develops and deploys industrial artificial intelligence (AI) technologies at scale, focused on the energy sector. As a venture between Presight (G42) and ADNOC, AIQ has productized 15 AI-enabled solutions that support clients to perform better, protect teams and equipment, keep operations sustainable, and rapidly scale successes. The organization embraces an innovative and entrepreneurial spirit, looking to push boundaries and solve transformational challenges across industry. It welcomes professionals that share the desire to make meaningful and impactful contributions to the mission to deliver responsible AI to the heart of industrial processes. Always on the forefront of technology, AIQ provides its talent with an environment to thrive and excel. Working at AIQ includes participating in some of the most significant industrial transformation projects, interacting with massive data pools, utilizing sophisticated AI infrastructure that is powered by a NVIDIA GPU cloud computing platform, and access to abundant computing, storage, and network resources made available from across the G42 ecosystem.\nOverview:\nPurpose of the role\nAIQ is building RoboWell APC, the autonomous execution layer of its production platform: a model-predictive control (MPC) engine that runs on edge hardware inside the operator’s OT environment and closes the loop on wells in real time. The first target is smart completions - wells equipped with downhole inflow control valves (ICVs), permanent downhole gauges and surface chokes - where the APC will continuously re-optimise zonal inflow and lift settings against reservoir, well-integrity and facility constraints, with the control room supervising rather than driving.\nWe are looking for a hands-on technical lead who can take this from control-algorithm design and simulator validation all the way to a hardened, containerised, cyber-compliant deployment on field edge devices, integrated with the DCS/SCADA and with AIQ’s IT layer (NextGenCCR, AiPSO, the IT/OT bridge). You will own the software architecture of the edge controller and be the engineer who makes it run safely on real wells.\nResponsibilities:\nKey responsibilities\nControl & optimisation\nDesign and implement MPC / constrained-optimisation controllers for multi-zone smart-completion wells (ICV position, choke, gas-lift or ESP set-points), including the process models, constraint handling, disturbance estimation and objective functions they rely on.\nBuild the supervisory logic around the controller: operating envelopes, well-integrity and equipment limits, mode management (auto / advisory / manual), bumpless transfer, fallback and safe-state behaviour.\nWork with production and reservoir engineers to translate operating philosophy and constraints into controller behaviour, and to define the KPIs the APC is judged on.\nEdge software & deployment\nOwn the edge software architecture: soft-real-time execution in C++ / Python on industrial edge hardware (Linux), containerised (Docker / OCI), with deterministic scheduling, watchdogs, state persistence and remote update.\nImplement the southbound integration to the control system - OPC UA, Modbus/TCP, DNP3 or vendor protocols - with read-back verification, write-permission handling and full change traceability.\nImplement the northbound integration to the AIQ IT layer through the IT/OT bridge (policy-enforced fetcher, DMZ / containment zone): model and configuration distribution, telemetry and observability from every hop back to the control centre.\nMeet operator OT cybersecurity requirements (IEC 62443 zoning, hardening, signed artefacts, secrets handling, audit logging) and support site surveys, FAT/SAT and cyber approvals for deployment.\nVerification & field delivery\nBuild and maintain the SIL/HIL test environment: closed-loop testing of the APC against well and network simulators and against physical controllers before anything is written to a live well.\nLead the field pilots on smart-completion wells with the operator’s control-room and production teams: commissioning, tuning, performance monitoring and hand-over to operations.\nEstablish CI/CD-to-device pipelines, release management and documentation (SDDs, interface control documents, test reports) to the standard expected by the operator’s architecture and cyber teams.\nTechnical leadership\nSet the technical direction of the edge / APC workstream and mentor developers and data engineers working on it; review designs and code.\nRepresent the edge architecture in front of operator architects, OT / cybersecurity teams and AIQ leadership; contribute to proposals and roadmaps for RoboWell APC across ESP, gas-lift and natural-flow wells.\n\nQualifications:\nRequired qualifications and experience\nMSc (or equivalent) in Control Engineering, Robotics, Electronic / Electrical Engineering, Computer Science or a related field.\n8+ years building software for autonomous or mission-critical control systems, with a track record of taking a system from concept through simulation and HIL to real-hardware deployment and field validation.\nStrong practical MPC / optimal-control background: formulating and solving constrained MPC problems, state estimation (Kalman / MHE), model identification and controller tuning on real plant.\nExpert C++ (C++17 or later) and Python on Linux, including multithreading, soft-real-time design, memory and latency optimisation, and profiling on constrained edge hardware.\nHands-on with industrial / field communication protocols (OPC UA, Modbus, DNP3, MQTT) and with interfacing software to PLC / DCS / SCADA or flight-controller-class hardware.\nExperience with containerised on-device deployment, CI/CD pipelines, Git-based workflows and observability (structured logging, metrics, telemetry).\nExperience designing and using SIL / HIL test environments and simulators for closed-loop verification.\nFluent English; able to write clear architecture and interface documents and present to technical and non-technical stakeholders.\nDesirable\nExposure to oil & gas production operations - smart completions / ICVs, artificial lift (ESP, gas lift), well and network simulators (e.g. PIPESIM, OLGA, Prosper/GAP) - or willingness to learn the domain quickly.\nFamiliarity with OT cybersecurity practice: IEC 62443, DMZ / data-diode architectures, device hardening, signed and verified software distribution.\nApplied reinforcement learning or learning-based control, and experience combining ML models with classical controllers in a safe, supervised manner.\nExperience with NVIDIA Jetson or similar accelerated edge platforms, and with ROS 2 / DDS middleware.\nPrior experience in a lead or architect role delivering software into a regulated or safety-critical environment (aerospace, defence, energy, utilities).\nWhat success looks like in the first 12 months\nAn APC edge stack that passes closed-loop SIL/HIL validation against the smart-completion well simulator and the operator’s cyber and architecture review.\nRoboWell APC running in closed loop on a first set of smart-completion wells, with measured production uplift or constraint-compliance improvement and a documented hand-over to the control room.\nA reusable, documented edge platform (deployment, protocol adapters, observability, update path) that the wider RoboWell team can extend to ESP and gas-lift wells.\n\nWhat working at AIQ offers:\nCulture: Encouraging initiative, work in an environment that is fast-paced and varied, surrounded by talented peers from around the world, who are similarly attracted to applying their skills to solve transformational challenges.\nCareer: Join a team in which your contribution is recognized and rewarded, while you are supported to operate at your peak performance.\nRewards: An attractive renumeration package that includes healthcare, education support for dependents, leave benefits, and 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