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ai solutions engineer

Contango

<div>
 
 Contract duration:
 
 6 months (with a potential extension)
 
 
 
 Engagement Type:
 
 Full-time
 
 
 
 Start date:
 
 July 2026
 
 
 
 Location:
 
 Abu Dhabi (on-site)
 
 
 
 Role Overview
 
 
 
 We are seeking an experienced AI Engineer to design, build, and deploy production-grade AI systems &mdash; primarily LLM-powered applications, RAG pipelines, and agentic workflows &mdash; that solve real client business problems in the UAE. The role combines applied AI expertise with solid software and data engineering foundations, with hands-on ownership from prototype to production and hypercare.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Design and deliver end-to-end LLM applications: scope the use case, build, evaluate, deploy, and hand over reliable, observable services.
 </li>
 <li>
 Build and optimize RAG pipelines: data parsing, ingestion, chunking, embeddings, vector search, re-ranking, and context-window optimization.
 </li>
 <li>
 Design and orchestrate agentic workflows for reasoning, planning, and task execution.
 </li>
 <li>
 Build evaluation and observability: define metrics (accuracy, latency, cost-per-task), run eval pipelines, and monitor systems in production.
 </li>
 <li>
 Integrate AI components into client applications via secure backend APIs; collaborate with data, software, and DevSecOps teams.
 </li>
 <li>
 Engineer the supporting data layer: pipelines, feature/embedding stores, and data quality for AI workloads on cloud platforms.
 </li>
 <li>
 Manage the full lifecycle: deployment, monitoring, versioning, rollback readiness, and cost/performance optimization.
 </li>
 <li>
 Translate business problems into AI solutions and communicate model capabilities, limitations, and trade-offs clearly to clients and stakeholders.
 </li>
 <li>
 Maintain technical documentation, runbooks, and delivery artifacts across Design &amp; Align &rarr; Build &rarr; UAT &rarr; Production &amp; Hypercare.
 
 
 </li>
 </ul>
 
 Candidate Requirements
 
 
 
 <ul>
 <li>
 Demonstrated experience delivering AI solutions in an enterprise or client-facing/consulting environment.
 </li>
 <li>
 Prior experience at management consulting firms and/or Big Tech is an advantage.
 </li>
 <li>
 Strong Python and solid software engineering fundamentals.
 </li>
 <li>
 Hands-on LLM APIs (Anthropic, OpenAI, Azure OpenAI), including tokenization, latency, and cost control.
 </li>
 <li>
 Proven RAG architecture experience with vector databases (e.g., Azure AI Search, Qdrant, Chroma, Pinecone, FAISS).
 </li>
 <li>
 Experience with agent/orchestration frameworks (e.g., LangChain, LangGraph, CrewAI).
 </li>
 <li>
 Evaluation and observability skills for production AI (eval pipelines, LLM-as-judge, monitoring).
 </li>
 <li>
 Working knowledge of a major cloud AI/data stack (Azure, AWS, or GCP) and MLOps tooling (e.g., MLflow, Docker).
 </li>
 <li>
 Solid data engineering skills: SQL, distributed processing (e.g., Spark/Databricks).
 </li>
 <li>
 Strong collaboration and communication skills &mdash; able to work with both technical teams and senior client stakeholders.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
 </li>
 <li>
 3&ndash;6 years of experience building and shipping AI/ML solutions into production.
 </li>
 <li>
 Preferred certifications (nice to have): cloud AI certifications (Azure AI Engineer, AWS ML, Databricks GenAI Engineer).
 </li>
 </ul>
</div>

AI Engineer (6 months contract)

TALENTMATE

<div>
 
 Job Description
 
 
 
 
 Contract duration:
 
 6 months (with a potential extension)
 
 
 
 Engagement Type:
 
 Full-time
 
 
 
 Start date:
 
 July 2026
 
 
 
 Location:
 
 Abu Dhabi (on-site)
 
 
 
 Role Overview
 
 
 
 We are seeking an experienced AI Engineer to design, build, and deploy production-grade AI systems &mdash; primarily LLM-powered applications, RAG pipelines, and agentic workflows &mdash; that solve real client business problems in the UAE. The role combines applied AI expertise with solid software and data engineering foundations, with hands-on ownership from prototype to production and hypercare.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Design and deliver end-to-end LLM applications: scope the use case, build, evaluate, deploy, and hand over reliable, observable services.
 </li>
 <li>
 Build and optimize RAG pipelines: data parsing, ingestion, chunking, embeddings, vector search, re-ranking, and context-window optimization.
 </li>
 <li>
 Design and orchestrate agentic workflows for reasoning, planning, and task execution.
 </li>
 <li>
 Build evaluation and observability: define metrics (accuracy, latency, cost-per-task), run eval pipelines, and monitor systems in production.
 </li>
 <li>
 Integrate AI components into client applications via secure backend APIs; collaborate with data, software, and DevSecOps teams.
 </li>
 <li>
 Engineer the supporting data layer: pipelines, feature/embedding stores, and data quality for AI workloads on cloud platforms.
 </li>
 <li>
 Manage the full lifecycle: deployment, monitoring, versioning, rollback readiness, and cost/performance optimization.
 </li>
 <li>
 Translate business problems into AI solutions and communicate model capabilities, limitations, and trade-offs clearly to clients and stakeholders.
 </li>
 <li>
 Maintain technical documentation, runbooks, and delivery artifacts across Design &amp; Align &rarr; Build &rarr; UAT &rarr; Production &amp; Hypercare.
 
 
 </li>
 </ul>
 
 Candidate Requirements
 
 
 
 <ul>
 <li>
 Demonstrated experience delivering AI solutions in an enterprise or client-facing/consulting environment.
 </li>
 <li>
 Prior experience at management consulting firms and/or Big Tech is an advantage.
 </li>
 <li>
 Strong Python and solid software engineering fundamentals.
 </li>
 <li>
 Hands-on LLM APIs (Anthropic, OpenAI, Azure OpenAI), including tokenization, latency, and cost control.
 </li>
 <li>
 Proven RAG architecture experience with vector databases (e.g., Azure AI Search, Qdrant, Chroma, Pinecone, FAISS).
 </li>
 <li>
 Experience with agent/orchestration frameworks (e.g., LangChain, LangGraph, CrewAI).
 </li>
 <li>
 Evaluation and observability skills for production AI (eval pipelines, LLM-as-judge, monitoring).
 </li>
 <li>
 Working knowledge of a major cloud AI/data stack (Azure, AWS, or GCP) and MLOps tooling (e.g., MLflow, Docker).
 </li>
 <li>
 Solid data engineering skills: SQL, distributed processing (e.g., Spark/Databricks).
 </li>
 <li>
 Strong collaboration and communication skills &mdash; able to work with both technical teams and senior client stakeholders.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
 </li>
 <li>
 3&ndash;6 years of experience building and shipping AI/ML solutions into production.
 </li>
 <li>
 Preferred certifications (nice to have): cloud AI certifications (Azure AI Engineer, AWS ML, Databricks GenAI Engineer).
 
 
 </li>
 </ul>
 Job Details
 
 
 Role Level: Not Applicable Work Type: Contract Country: United Arab Emirates City: Abu Dhabi Company Website: https://contango.ae/ Job Function: Data Science &amp; AI Company Industry/
 
 
 Sector: Other
 
 
 What We Offer
 
 
 
 About The Company
 
 
 
 Searching, interviewing and hiring are all part of the professional life. The TALENTMATE Portal idea is to fill and help professionals doing one of them by bringing together the requisites under One Roof. Whether you're hunting for your Next Job Opportunity or Looking for Potential Employers, we're here to lend you a Helping Hand.
 
 
 Report
 
 
 Disclaimer: talentmate.com is only a platform to bring jobseekers &amp; employers together. Applicants are advised to research the bonafides of the prospective employer independently. We do NOT endorse any requests for money payments and strictly advice against sharing personal or bank related information. We also recommend you visit Security Advice for more information. If you suspect any fraud or malpractice, email us at abuse@talentmate.com.
</div>

AI Engineer 6 Months Contract

Confidential Careers

<div>
 
 
 About the Company
 
 
 
 We are seeking technically strong AI Engineers to support the development of AI-powered applications, agents, and automation solutions across internal and client-facing engagements.
 
 
 
 
 
 
 About the Role
 
 
 
 This is a hands-on engineering role focused on building and deploying production-ready AI solutions. We are looking for builders with strong technical depth in LLM integration, agent orchestration, workflow automation, and AI application development. This is not a strategy or advisory role &mdash; demonstrable build capability is essential.
 
 
 
 
 
 
 Responsibilities
 
 
 <ul>
 <li>
 Design and build AI-powered applications, agents, and automation tools
 </li>
 <li>
 Develop AI solutions using modern LLM APIs such as OpenAI, Anthropic, or equivalent
 </li>
 <li>
 Build agentic workflows, tool-use pipelines, and orchestration frameworks
 </li>
 <li>
 Develop RAG pipelines, prompt engineering strategies, and knowledge-based AI solutions
 </li>
 <li>
 Build workflow automations connecting AI models, APIs, and enterprise systems
 </li>
 <li>
 Develop backend services, APIs, and data pipelines using Python and/or Node.js
 </li>
 <li>
 Deploy and maintain AI applications on cloud infrastructure (AWS, Azure, GCP)
 </li>
 <li>
 Integrate structured and unstructured data sources, vector databases, and knowledge bases
 </li>
 </ul>
 
 
 
 
 
 Qualifications
 
 
 <ul>
 <li>
 Demonstrable portfolio of AI applications, agents, or automation tools built and deployed
 </li>
 </ul>
 
 
 
 
 
 Required Skills
 
 
 <ul>
 <li>
 Strong hands-on experience with:
 </li>
 <li>
 LLM APIs
 </li>
 <li>
 Prompt engineering
 </li>
 <li>
 RAG pipelines
 </li>
 <li>
 Agent orchestration frameworks
 </li>
 <li>
 Workflow automation
 </li>
 <li>
 Strong backend engineering skills in Python and/or Node.js
 </li>
 <li>
 Experience with AI engineering frameworks such as:
 </li>
 <li>
 LangChain
 </li>
 <li>
 LangGraph
 </li>
 <li>
 CrewAI
 </li>
 <li>
 AutoGen
 </li>
 <li>
 Equivalent tools
 </li>
 <li>
 Experience deploying production applications in cloud environments
 </li>
 <li>
 Fast execution mindset and ability to build functional solutions quickly
 </li>
 </ul>
 
 
 
 
 Preferred Skills
 
 
 <ul>
 <li>
 Frontend experience (React, Next.js or similar)
 </li>
 <li>
 Consulting or client-facing delivery experience
 </li>
 <li>
 Experience building AI tools for enterprise workflows or operations
 </li>
 </ul>
 
 
</div>

AI Engineer

Qualcomm

<div><h2 class="jobSectionHeader">Company:
</h2>Qualcomm Engineering FZ LLC
<h2 class="jobSectionHeader">Job Area:
</h2>Engineering Group, Engineering Group &gt; Software Engineering
General Summary:
<div><div>About Us
</div><div>Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including intelligent edge devices, next-generation computing platforms, and advanced AI solutions. Qualcomm&rsquo;s leadership in AI, high ‑ performance compute, and connectivity is driving innovation across cloud, edge, and data center environments - delivering scalable, power ‑ efficient platforms that power the next generation of intelligent infrastructure.
</div><div>About the role
</div><div>We are seeking a Staff Solutions Engineer to support executing the portfolio of edge initiatives by translating customer opportunities into shippable solutions - from solution definition to demo/pilot execution and into commercial deployment readiness. The ideal candidate is a self-starter who can operate independently, lead technical execution with minimal supervision, and serve as a trusted technical partner to sales, customers, and internal engineering teams .
</div><div>This role spans the full lifecycle: pre ‑ sales solutioning, sales enablement, and post ‑ sales technical success, with direct accountability for technical outcomes in demos, PoCs , pilots, and scaled rollouts across edge initiatives such as edge AI devices and edge infrastructure use cases .
</div><div>You should have strong expertise in AI models, quantization, performance optimization, and deployment, along with the ability to define architecture, size workloads, and design systems. They should also have experience developing deep learning models across hardware platforms, strong programming skills, the ability to work effectively with cross-functional teams, and proficiency in machine learning frameworks, Linux, and container orchestration tools. 
</div><div>What You&rsquo;ll Do
</div><div>Solutioning &amp; Technical Business Development (Pre ‑ Sales)
</div><div><ul><li>Lead discovery with internal stakeholders and end customers to capture requirements, define success criteria, and shape opportunities into a clear solution scope, architecture, and execution plan.
</li></ul></div><div><ul><li>Create solution blueprints that integrate edge compute + AI workloads + connectivity, including feasibility assessments and &ldquo;fit-to-platform&rdquo; recommendations.
</li></ul></div><div><ul><li>Build and maintain reusable technical assets (reference architectures, demo scripts, BOM guidance, integration patterns) that accelerate field execution and repeatability.
</li></ul></div><div><ul><li>Partner with internal engineering/product teams to translate customer needs into actionable technical requirements, while providing structured feedback based on field learnings.
</li></ul></div><div>Sales Support &amp; Deal Acceleration (Sales Cycle)
</div><div><ul><li>Support sales by leading technical engagements: architecture reviews, technical workshops, competitive positioning discussions, and stakeholder presentations , ensuring technical clarity and risk closure.
</li></ul></div><div><ul><li>Own technical responses for customer-facing deliverables (e.g., technical proposals, solution narratives, pilot plans), ensuring alignment to commercialization goals and operational requirements
</li></ul></div><div><ul><li>Coordinate cross-functional execution (BD, engineering, PMO, partners) to keep pilots and deal-critical milestones on-track and unblock dependencies.
</li></ul></div><div>Demo / Pilot Leadership (PoC Pilot)
</div></div><div><div><ul><li>Design, build, and lead end-to-end demos and pilots, including environment setup, integration, performance validation, and customer walkthroughs
</li></ul></div><div><ul><li>Own pilot execution governance: scope control, success criteria, issue triage, and closure readiness - driving toward a decision point for scale.
</li></ul></div><div><ul><li>Enable customer and partner teams via structured technical training and practical handover artifacts.
</li></ul></div><div>Commercial Deployments &amp; Post ‑ Sales Technical Success
</div><div><ul><li>Support transition from pilot to production by defining deployment prerequisites (security, monitoring, lifecycle ops, integration hardening) and ensuring &ldquo;production-ready&rdquo; technical acceptance.
</li></ul></div><div><ul><li>Act as the technical escalation point during early deployments, driving issue resolution across internal teams and partners, and maintaining customer confidence through clear communication.
</li></ul></div><div><ul><li>Establish repeatable rollout playbooks (deployment patterns, validation checklists, operational handover templates) to support scale across multiple opportunities. 
</li></ul></div><div>Required Qualifications
</div><div><ul><li>Bachelor&rsquo;s degree in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
</li></ul></div><div><ul><li>10 + years in customer-facing engineering, solutions engineering, field applications, systems engineering, or technical BD roles spanning the full cycle (discovery PoC/pilot deployment/scale)
</li></ul></div><div><ul><li>Proven ability to build and support AI embedded / edge deployments, including ecosystem integration experience across customer engineering, internal business units, and partners to drive aligned, end-to-end execution.
</li></ul></div><div><ul><li>Hands-on experience delivering products/solutions in an applied setting (platform bring-up, system integration, performance validation, deployment support , and field issue resolution )
</li></ul></div><div><ul><li>Strong understanding of edge/IoT solution stacks: device + OS + middleware + connectivity + cloud/edge integration + security considerations.
</li></ul></div><div><ul><li>Strong experience with AI frameworks such as PyTorch and TensorFlow, including deploying and validating AI/ML and computer vision workloads on edge devices, with hands-on performance tuning, quality troubleshooting, and field validation.
</li></ul></div><div><ul><li>Experience deploying and optimizing inference on hardware accelerators ( CPU/GPU/ASIC /NPU) , with understanding of inference systems and optimization for edge AI and/ or Data Center platforms
</li></ul></div><div><ul><li>Excellent C/C++/Python (or equi valent) programming and software design skills, including debugging, profiling, and performance analysis.
</li></ul></div><div><ul><li>Solid understanding of inference hardware constraints and s ystem - level performance bottlenecks (memory bandwidth, latency/throughput tradeoffs, thermal/power constraints).
</li></ul></div><div><ul><li>Hands - on expertise with Linux-based systems, low level software, drivers, and systems bring up.
</li></ul></div></div><div><div><ul><li>E xperience developing/deploying Linux solutions using containers and orchestration (e.g., Docker/Kubernetes), plus source code/config management (Git).
</li></ul></div><div><ul><li>Excellent communication skills: ability to translate complex technical details into clear customer-facing narratives and executive-ready updates.
</li></ul></div><div><ul><li>Ability to serve as the in- timezone , on-the-ground technical owner across multiple concurrent programs, driving structured issue triage, escalations, and reducing delivery risk through tight cross-team alignment . 
</li></ul></div><div>Preferred Qualifications
</div><div><ul><li>Experience enabling, deploying, or supporting solutions on Qualcomm SoC-based systems (IoT/edge/compute) and translat ing customer requirements into actionable platform execution.
</li></ul></div><div><ul><li>Exposure to Qualcomm compute ecosystems (e.g., Snapdragon-class platforms), including subsystem-level understanding (CPU/GPU/NPU/modem/connectivity) and customer enablement patterns.
</li></ul></div><div><ul><li>Familiarity with Qualcomm accelerated inference platforms ( e.g AI100) or comparable data center / edge inference accelerators, with strong end-to-end architecture understanding across edge-to-core environments.
</li></ul></div><div><ul><li>Understanding of wireless connectivity considerations for edge deployments, including cellular communication technologies and filed integration dependencies . 
</li></ul></div><div>Why Join Qualcomm
</div><div>At Qualcomm, you&rsquo;ll work at the intersection of AI silicon, system architecture, and real ‑ world deployment. You will engage directly with strategic customers, influence next ‑ generation AI data center platforms, and help define scalable, power ‑ efficient infrastructure for the AI era. This role provides a unique opportunity to shape both technology direction and customer outcomes, while working with world ‑ class engineering and product teams.
</div></div>What's on Offer
Apart from working with great people, we offer the below:
<ul><li>Salary including housing &amp; transport allowance
</li><li>Stock (RSU's) and performance related bonus
</li><li>16 weeks fully paid Maternity Leave
</li><li>6 weeks fully paid Paternity Leave
</li><li>Employee stock purchase scheme
</li><li>Child Education Allowance
</li><li>Relocation and immigration support (if needed)
</li><li>Life and Medical Insurance
</li><li>Live+ Well Reimbursement for health and recreational membership fees
</li></ul>Minimum Qualifications:
<ul><li>Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience.
</li></ul>OR
 
Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.
 
OR
 
PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
 
 
<ul><li>2+ years of work experience with Programming Language such as C, C++, Java, Python, etc.
</li></ul>*References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.
<div>Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here . Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).
</div>Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
To all Staffing and Recruiting Agencies : Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.
If you would like more information about this role, please contact Qualcomm Careers .</div>

Customer Engineering, Field Engineer & Solutions Architect, Staff - UAE

Open Innovation AI

<div>
 
 
 Company Overview
 
 
 
 
 
 
 Open Innovation AI is a global technology company that specializes in developing advanced solutions for managing AI workloads. Its flagship product, the Open Innovation Cluster Manager (OICM), orchestrates complex AI tasks efficiently across diverse infrastructures. The platform is hardware-agnostic, optimized for various GPUs and accelerators hardware, and facilitates seamless integration and scalability for enterprise AI applications. Open Innovation AI focuses on optimizing and simplifying AI workload management and making AI technologies accessible to organizations of all sizes. With its innovative solutions, companies can reduce operational costs, accelerate time to value, and maximize their return on investment, ensuring that their AI strategies contribute directly to enhanced business outcomes
 
 
 
 
 
 
 Role Overview:
 
 
 
 
 
 
 You will work directly with customers on their hardest problems, then design and build AI systems to solve them.
 
 
 Most of what you build will sit on top of the OI Platform, which provides the underlying infrastructure: model inference, agent orchestration, retrieval and the broader AI tooling layer. Your focus is not on rebuilding the foundation, but on applying it effectively. You will concentrate on the customer-specific layer: workflows, data integrations, user interfaces, and the points where the platform meets a live operation.
 
 
 Teams are small, autonomy is high, and you own each project from kickoff through production. A typical day may involve discussing architecture with a colleague, preparing customer data, building a web application on top of the platform, integrating with SAP or Oracle, prototyping an agent, and presenting results to executives.
 
 
 
 
 
 **You build it, you ship it, you own it**
 
 
 
 
 
 This is a strong environment for engineers who want to develop real depth. You will learn how to break down ambiguous problems, evaluate real tradeoffs, work directly with modern AI tooling, and grow within a team that will challenge you.
 
 
 
 
 
 
 Role Responsibilities:
 
 
 
 
 
 <ul>
 <li>
 Embed with the client and work alongside their stakeholders to translate business problems into technical solutions.
 </li>
 <li>
 Build AI solutions on top of the OI Platform, including agent workflows, RAG pipelines, custom applications, and data products that connect the platform to the customer's enterprise systems.
 </li>
 <li>
 Own the full lifecycle: discovery, architecture, build, deployment, and iteration once real users are on the system.
 </li>
 <li>
 Transform large, complex, real-world data into formats that AI systems can use effectively.
 </li>
 <li>
 Collaborate with product engineers, deployment strategists, and the customer's team on both the solution and the surrounding strategy.
 </li>
 <li>
 Travel to client sites when the work calls for it: running workshops, debugging live systems, and building trust with the people you are delivering for
 </li>
 </ul>
 
 
 
 
 
 Required skills &amp; Experience:
 
 
 
 
 
 <ul>
 <li>
 A strong engineering background in computer science, software engineering, mathematics, physics, or data science.
 </li>
 <li>
 4+ years in backend, data, or full-stack engineering. More senior and more junior candidates are welcome to apply; please indicate where you sit.
 </li>
 <li>
 Genuine proficiency in Python or TypeScript (either is acceptable).
 </li>
 <li>
 Hands-on experience with modern AI tooling: LLMs, RAG, vector databases, agent frameworks, evaluations, and prompt orchestration.
 </li>
 <li>
 Experience working with large-scale data, cloud platforms (AWS, GCP, or Azure), and modern data tooling.
 </li>
 <li>
 The ability to hold a substantive conversation with a CTO and a non-technical executive in the same meeting.
 </li>
 <li>
 A strong sense of ownership: you make decisions, ship work, and move forward without waiting to be directed.
 </li>
 <li>
 Comfort with ambiguity, shifting priorities, and problems that have not been solved before
 </li>
 </ul>
 
 
 
 
 
 Preferred Experience:
 
 
 
 
 
 <ul>
 <li>
 Experience deploying AI/ML systems in regulated or mission-critical environments.
 </li>
 <li>
 Experience working directly with enterprise customers in a delivery or consulting capacity
 </li>
 </ul>
 
 
</div>

Forward Deployed Engineer

<div>
 
 
 We&rsquo;re Hiring: AI Engineer | UAE N
 
 
 
 We are looking for a passionate and hands-on
 
 AI Engineer
 
 to join our growing team and help build cutting-edge AI-powered solutions.
 
 
 
 
 
 
 About the Role
 
 
 
 As an AI Engineer, you will be responsible for
 
 designing, building, and deploying AI-driven features and applications
 
 using large language models (LLMs) such as GPT and Claude. This is a highly practical role focused on
 
 developing real-world AI products
 
 , not research or model training.
 
 
 
 
 
 
 What You&rsquo;ll Do
 
 
 <ul>
 <li>
 Develop and deploy AI-powered applications from prototype to production
 </li>
 <li>
 Integrate LLMs (GPT, Claude) into business solutions using APIs
 </li>
 <li>
 Design prompts, workflows, and AI agents to improve output quality
 </li>
 <li>
 Work with Azure AI Foundry and modern AI platforms
 </li>
 <li>
 Continuously evaluate and adopt new AI tools and technologies
 </li>
 <li>
 Collaborate with product, design, and engineering teams
 </li>
 </ul>
 
 
 
 
 
 What We&rsquo;re Looking For
 
 
 <ul>
 <li>
 Degree in Computer Science, AI, or related field
 </li>
 <li>
 Strong Python and software engineering skills
 </li>
 <li>
 Experience working with APIs and LLM integrations
 </li>
 <li>
 Hands-on experience with GPT, Claude, or similar models
 </li>
 <li>
 Familiarity with tools like Git and modern AI frameworks
 </li>
 <li>
 Ability to work in a fast-paced, evolving environment
 </li>
 </ul>
 
 
 
 
 
 Nice to Have
 
 
 <ul>
 <li>
 Experience with LangChain, LlamaIndex, or similar frameworks
 </li>
 <li>
 Knowledge of RAG, vector databases, or Azure AI tools
 </li>
 <li>
 Exposure to open-source LLMs (Hugging Face, Ollama)
 </li>
 </ul>
</div>

Artificial Intelligence Engineer

ENEC Operations

<div>
 
 The Data and AI Engineering Expert is a senior hands-on technical authority responsible for the design, development, and operationalization of enterprise data and AI platforms across ENEC. This role leads the engineering delivery of scalable, secure, and compliant data pipelines, AI/ML solutions, and analytics platforms leveraging technologies such as Databricks (Delta Lake, Unity Catalog, MLflow, Delta Live Tables), Microsoft Azure (Azure Data Factory, Azure ML, Microsoft Fabric, Azure AI Foundry, Copilot Studio), Collibra (Data Governance &amp; Data Quality), Power BI, and OT/industrial data systems including PI System, SCADA, and DCS.
 
 
 
 
 
 The Expert applies deep engineering expertise to architect and deliver production-grade solutions, ensures data governance and quality standards are upheld, and drives the adoption of AI and analytics capabilities that directly support ENEC's mission of safe, innovative, and efficient nuclear energy generation. This role acts as a principal technical contributor and mentor, enabling operational excellence and digital transformation at scale.
 
 
 Activity: Data Engineering Architecture &amp; Platform Development
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Design, build, and maintain scalable, secure, and high-performance data platforms including Lakehouse architectures using Databricks Delta Lake, Unity Catalog, and Delta Live Tables (DLT).
 
 
 &bull; Develop and operationalize robust data pipelines, ETL/ELT workflows, and integration frameworks using Azure Data Factory, Databricks Workflows, and related orchestration tools.
 
 
 &bull; Architect and implement data models, semantic layers, and data products that serve enterprise analytics, AI, and reporting needs.
 
 
 &bull; Ensure seamless integration of OT and industrial data sources &mdash; including PI System, SCADA, and DCS &mdash; into enterprise data platforms.
 
 
 &bull; Design and implement APIs, data contracts, and integration frameworks to enable enterprise-wide data consumption.
 
 
 &bull; Ensure platform scalability, resilience, high availability, and disaster recovery capabilities in compliance with nuclear and regulatory requirements.
 
 
 &bull; Apply and enforce data standards, naming conventions, and metadata management practices across all data assets using Collibra and Unity Catalog.
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Design, develop, and deploy production-grade machine learning, deep learning, and generative AI models using Azure ML, MLflow, and Databricks ML.
 
 
 &bull; Engineer end-to-end MLOps pipelines covering model training, versioning, validation, deployment, monitoring, and retraining &mdash; ensuring reliability and reproducibility.
 
 
 &bull; Build and operationalize AI-powered solutions including predictive analytics, anomaly detection, natural language processing (NLP), and GenAI/LLM-based applications using Azure AI Foundry and Copilot Studio.
 
 
 &bull; Develop BI and semantic models in Microsoft Fabric and Power BI, enabling self-service analytics and executive reporting across the organization.
 
 
 &bull; Apply Responsible AI principles and governance frameworks to all AI and ML solutions, ensuring explainability, fairness, and compliance.
 
 
 &bull; Conduct rigorous model evaluation, performance benchmarking, and validation to ensure solutions meet accuracy, reliability, and safety standards.
 
 
 &bull; Contribute to the continuous improvement of AI engineering standards, patterns, and reusable components across the enterprise.
 
 
 Activity: Advanced Subject Matter Knowledge
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Demonstrates deep, hands-on expertise in enterprise data engineering, AI/ML platform development, and cloud-native architectures within highly regulated environments.
 
 
 &bull; Maintains current knowledge of global trends in data engineering, MLOps, GenAI, cloud technologies (Azure, hybrid, on-premise), and nuclear industry compliance requirements.
 
 
 &bull; Understands the full lifecycle of data and AI platforms including design, development, testing, deployment, monitoring, and decommissioning.
 
 
 &bull; Applies comprehensive knowledge of data governance frameworks (Collibra, Unity Catalog), master data management, data quality, and lineage management.
 
 
 &bull; Possesses strong awareness of OT/IT convergence, industrial data systems (PI System, SCADA, DCS), and their integration into enterprise analytics environments.
 
 
 &bull; Maintains expert-level proficiency in programming languages and frameworks including Python, SQL, Spark, and relevant AI/ML libraries.
 
 
 Activity: Complex Problem Solving &amp; Innovation
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Tackles complex, ambiguous engineering challenges in data platform architecture, AI model development, and system integration &mdash; delivering innovative, compliant, and production-ready solutions.
 
 
 &bull; Leads root cause analysis and corrective/preventive actions for platform incidents, data quality failures, model degradation, and system disruptions.
 
 
 &bull; Develops and implements robust engineering solutions to address performance bottlenecks, scalability constraints, and integration challenges.
 
 
 &bull; Navigates conflicting technical requirements, evolving regulatory standards, and stakeholder needs to deliver optimal engineering outcomes.
 
 
 &bull; Drives continuous improvement by benchmarking against global engineering best practices, evaluating emerging technologies, and implementing lessons learned.
 
 
 &bull; Leads proof-of-concept and pilot initiatives to evaluate new data and AI technologies and engineering approaches
 
 
 
 
 
 Activity: Applied Skills &amp; Professional Know-How
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Expertly engineers, deploys, and operates scalable, secure, and compliant data and AI platforms across Databricks, Azure, and OT environments.
 
 
 &bull; Applies hands-on proficiency in building data pipelines, ML models, GenAI solutions, BI semantic models, and governance frameworks in production environments.
 
 
 &bull; Mentors and develops junior and mid-level data engineers, data scientists, and analytics engineers &mdash; building future-ready technical capabilities.
 
 
 &bull; Leads technical design reviews, code reviews, and architecture assessments to uphold engineering quality and best practices.
 
 
 &bull; Effectively translates complex technical concepts into clear, actionable insights for both technical and non-technical stakeholders.
 
 
 &bull; Documents solutions, engineering patterns, and technical standards to build institutional knowledge and enable knowledge transfer.
 
 
 Activity: Security, Governance &amp; Compliance
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Design and enforce data security, access control, and privacy frameworks across all data and AI platforms using Unity Catalog, Collibra, and Azure security controls.
 
 
 &bull; Ensure all data engineering and AI solutions comply with national and international nuclear regulations, FANR requirements, and Responsible AI principles.
 
 
 &bull; Maintain comprehensive data lineage, audit trails, and documentation to support regulatory, audit, and governance requirements.
 
 
 &bull; Implement and enforce data quality frameworks and data governance policies using Collibra Data Quality and Unity Catalog.
 
 
 &bull; Conduct regular platform security assessments, data quality audits, and compliance reviews.
 
 
 &bull; Oversee risk management, audit readiness, and the implementation of robust technical controls for all platform operations.
 
 
 
 
 
 
 
 
 
 Minimum
 
 
 
 &bull; Bachelor&rsquo;s degree in computer science, Information Technology, Data Management, Engineering, or related discipline.
 
 
 &bull; Minimum 12+ years' hands-on experience in enterprise data engineering, AI/ML platform development, and cloud-native architectures, including production deployment and operations.
 
 
 &bull; Experience embedding governance within modern data platforms (preferably Databricks) and integrating SAP/Oracle ERP domains.
 
 
 
 
 
 
 
 
 
 Preferred
 
 
 
 
 
 
 &bull; Master&rsquo;s degree in a relevant field.
 
 
 &bull; Experience in energy, utilities, nuclear, financial services, government, or other highly regulated sectors.
 
 
 &bull; Demonstrated AI governance experience (provenance, bias monitoring, model data requirements) in data bricks or similar AI/analytics platforms exposure.
 
 
 &bull; Demonstrated experience leading large-scale data and AI engineering programs
 
 
 
 
 
 
 
 
 
 Certification -
 
 
 
 
 
 
 Multiple cloud and AI certifications. Microsoft Fabric, Azure AI Foundry, or Databricks Unity Catalog specialty certifications. Industry publications or contributions in data engineering or AI.
 
 
 
 
 
 Relevant certifications in one or more of: Databricks (Data Engineer Associate/Professional, ML Professional), Microsoft Azure (Data Engineer, AI Engineer, Solutions Architect), MLOps, or Data Governance (Collibra).
 
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Data and AI Engineering Expert

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