ai automation llms rag engineer

Sadat Consulting

Sen your CV across mohammed.sadatgac@gmail.com with below details1. Notice period 2. Current salary 3. Expected salary 4. Do you have Transferable IQAMARole Purpose Develop AI-enabled automation aligned with roadmap including automation workflows, AIassisted reporting, anomaly detection, and enterprise AI integration patterns.Key Responsibilities<ul><li>Develop automation pipelines and workflow automations</li><li>Integrate enterprise AI services (LLM APIs, automation endpoints)</li><li>Develop anomaly detection logic and operational intelligence rules</li><li>Build AI-assisted reporting components aligned with dashboards</li><li>Maintain AI/automation documentation and reproducibility</li><li>Ensure governance-aligned AI implementation (approval where required)</li><li>Support integration of AI outputs into enterprise apps (Frappe, dashboards)</li></ul>Mandatory Technical Requirements<ul><li>Python (ML/automation stack)</li><li>Experience integrating enterprise AI APIs</li><li>Familiarity with RAG architecture concepts</li><li>Automation frameworks and scripting (job scheduling, pipelines)</li><li>Strong integration skills (API-based orchestration)</li></ul>Minimum Experience<ul><li>8+ years experience</li><li>Proven delivery of AI/automation in enterprise environments</li></ul>Job Types: Full-time, PermanentWork Location: In person

AI Automation (LLMs, RAG) Engineer

SERVA

<div>Manual &amp; Automation QA Engineer
We're looking for a skilled QA Engineer who's equally comfortable crafting detailed test plans by hand and building robust automation frameworks. You'll ensure that every feature we ship meets the highest quality bar &mdash; including our growing suite of AI-powered capabilities.
Our Stack<ul><li>
Test Automation: Playwright / Cypress</li><li>
Unit &amp; Integration: Jest / Pytest</li><li>
API Testing: REST &amp; GraphQL</li><li>
Database: PostgreSQL</li><li>
DevOps: CI/CD Pipelines, GitHub Actions, AWS</li><li>
AI: AI Agents / LLMs (non-deterministic testing)</li></ul>
What You'll Do<ul><li>
Design, write, and maintain manual test plans and test cases for features across the full product</li><li>
Build and scale automation frameworks for regression, integration, and end-to-end testing</li><li>
Test AI-powered features including LLM outputs, agent behavior, and model responses for accuracy, safety, and consistency</li><li>
Collaborate closely with developers and product managers to shift QA left in the development lifecycle</li><li>
Define and track quality metrics, identify risk areas, and surface issues early before they reach production</li><li>
Develop evaluation strategies for non-deterministic systems &mdash; including prompt variation testing and output validation pipelines</li><li>
Participate in sprint planning and retrospectives as the quality voice on the team</li></ul>
What We're Looking For<ul><li>
2+ years of professional experience in software quality assurance, covering both manual and automation testing</li><li>
Hands-on experience testing AI agents, LLM-powered features, or ML models &mdash; understanding their non-deterministic nature is a must</li><li>
Proficiency in at least one modern automation framework (Playwright, Cypress, Selenium, or equivalent)</li><li>
Strong API testing skills using tools like Postman, REST Assured, or similar</li><li>
Experience working in agile environments with CI/CD pipelines and integrated test gates</li><li>
A methodical, curious mindset &mdash; you find edge cases others miss, and you document everything clearly</li><li>
Solid communication skills to write clear bug reports, test documentation, and quality summaries</li></ul>
Bonus Points If You&hellip;<ul><li>
Have experience evaluating agentic workflows, multi-step reasoning chains, or RAG pipelines</li><li>
Have worked with LLM evaluation frameworks like RAGAS, LangSmith, or Promptfoo</li><li>
Use AI coding tools like Cursor or Claude in your daily QA workflow &mdash; we actively encourage it</li><li>
Have built custom eval harnesses or synthetic test data generators using LLMs</li></ul>
AI-First Culture
We don't just build AI &mdash; we test it rigorously. Our QA engineers use Cursor and Claude to move faster, write better test cases, and automate the repetitive work. If you're already approaching quality assurance with AI tools in hand, you'll feel right at home.</div>

QA Engineer

DeepSource Technologies

<div>We are looking for a highly capable Senior AI Engineer / MLOps Engineer to join our team and lead the design, development, deployment, and optimization of scalable, production-grade AI and machine learning solutions. The ideal candidate will have strong hands-on experience across AI engineering, machine learning, MLOps, cloud-native architecture, and data engineering, with the ability to transform experimentation into reliable, business-ready systems.
This role requires deep expertise in LLMs, RAG, agentic AI workflows, CI/CD automation, production ML lifecycle management, and modern data platforms. The selected candidate will be expected to lead end-to-end AI initiatives, work across multiple projects, collaborate with technical and business stakeholders, and ensure operational excellence across AI platforms.
Key Responsibilities<ul><li>Design, develop, deploy, and maintain production-grade AI and machine learning systems end to end.</li><li>Build and optimize LLM-powered applications, including RAG pipelines, prompt workflows, agent-based systems, and multimodal AI use cases.</li><li>Develop intelligent workflows using tool-calling, orchestration frameworks, and contextual reasoning patterns.</li><li>Fine-tune, evaluate, and operationalize machine learning and foundation models for enterprise use cases.</li><li>Build and manage MLOps pipelines covering training, evaluation, model registration, deployment, monitoring, and retraining.</li><li>Implement CI/CD pipelines for ML and AI workflows to support automated testing, release management, and controlled deployments.</li><li>Establish model monitoring frameworks for drift detection, feature attribution, inference quality, and performance tracking.</li><li>Ensure reproducibility, reliability, and version control across AI/ML environments.</li><li>Architect scalable AI/ML platforms using modern compute, storage, orchestration, monitoring, and search services.</li><li>Build repeatable environments using Infrastructure as Code.</li><li>Support secure, high-availability, and cost-efficient deployment models across development, staging, and production environments.</li><li>Design scalable inference and serving patterns for variable workloads.</li><li>Build and maintain automated data pipelines, ETL/ELT workflows, and data processing frameworks for AI/ML consumption.</li><li>Ensure data quality, lineage, governance, and versioning to support dependable model training and inference.</li><li>Work with structured and unstructured datasets across data lakes, data warehouses, and operational systems.</li><li>Deliver analytics-ready datasets to downstream systems and applications.</li><li>Lead multiple AI initiatives in parallel, including planning, execution, and coordination with internal teams and stakeholders.</li><li>Work closely with product, engineering, data, and business teams to deliver production-ready AI capabilities.</li><li>Contribute to architecture decisions, technical documentation, best practices, and engineering standards.</li><li>Support knowledge sharing, technical leadership, and continuous improvement across the AI function. 
</li></ul> 
 
Requirements<ul><li> 
Bachelor&rsquo;s degree in Computer Engineering, Computer Science, Artificial Intelligence, Data Science, or a related field.</li><li>5+ years of hands-on experience in AI engineering, machine learning engineering, MLOps, or data/ML platform engineering.</li><li>Proven experience deploying production AI/ML solutions in enterprise environments.</li><li>Strong programming experience in Python and SQL.</li><li>Strong experience with enterprise AI/ML architecture and delivery.</li></ul></div>

Senior Ai Engineer - Riyadh,KSA

MASTER-WORKS

<div>Design, develop, and deploy Arabic-first Large Language Model (LLM) solutions to support AI-driven products and client projects, ensuring high-quality Arabic language understanding, generation, and contextual accuracy across enterprises.
Key Responsibilities1. LLM Development &amp; NLP (Core)<ul><li>
Design and fine-tune LLMs for Arabic language tasks
</li><li><ul><li>Text generation, classification, summarization
</li><li>Arabic grammar correction, dialect handling
</li></ul></li><li>Build and optimize RAG pipelines
</li><li>Develop prompt engineering strategies for Arabic + English contexts
</li></ul>2. Data &amp; Arabic Language Engineering<ul><li>
Build and curate Arabic datasets
</li><li>Handle:
</li><li><ul><li>Dialects (Gulf, MSA, etc.)
</li><li>Data cleaning, normalization
</li></ul></li><li>Define evaluation benchmarks for Arabic LLM quality
</li></ul>3. AI System Architecture<ul><li>
Develop LLM-powered systems using:
</li><li><ul><li>LangChain / LangGraph / Llama / OpenAI APIs
</li><li>Vector databases (ChromaDB, FAISS, etc.)
</li></ul></li><li>Integrate LLMs into:
</li><li><ul><li>APIs
</li><li>Enterprise systems
</li><li>AI agents / copilots
</li></ul></li></ul>4. Production &amp; MLOps<ul><li>
Deploy scalable AI systems using:
</li><li><ul><li>Docker, Kubernetes
</li><li>Cloud platforms (Azure, AWS, GCP)
</li></ul></li><li>Optimize:
</li><li><ul><li>Latency
</li><li>Cost
</li><li>Throughput
</li></ul></li></ul>5. Cross-functional Collaboration<ul><li>
Work with:
</li><li><ul><li>PMs (scope, delivery, timelines)
</li><li>BAs (requirements translation AI logic)
</li><li>QA (model validation &amp; test cases)
</li></ul></li><li>Support client demos and AI solution design
</li></ul>6. Governance Alignment (Critical for Nabeh)<ul><li>
Ensure:
</li><li><ul><li>AI outputs align with client expectations
</li><li>Traceability (data model output)
</li></ul></li><li>Support:
</li><li><ul><li>BRD validation (AI feasibility)
</li><li>UAT and acceptance criteria</li></ul></li></ul> 

Requirements 
 
Required SkillsTechnical<ul><li>
Strong in:
</li><li><ul><li>Python (PyTorch / TensorFlow)
</li><li>NLP &amp; LLMs (Transformers, RAG)
</li></ul></li><li>Experience with:
</li><li><ul><li>LangChain / LLM frameworks
</li><li>Vector databases
</li><li>Prompt engineering
</li></ul></li></ul>Arabic AI (Mandatory)<ul><li>
Native or fluent Arabic
</li><li>Experience in:
</li><li><ul><li>Arabic NLP
</li><li>Dataset preparation for Arabic
</li><li>Handling dialects
</li></ul></li></ul>Engineering &amp; Deployment<ul><li>
APIs (FastAPI / Flask)
</li><li>Microservices architecture
</li><li>Docker, Kubernetes
</li><li>CI/CD pipelines</li></ul>
Language Requirements<ul><li>
Arabic: Native / Fluent (MANDATORY)
</li><li>English: Professional (MANDATORY)
</li></ul>Preferred Qualifications<ul><li>
MSc or higher in AI / Data Science / NLP
</li><li>Experience in:
</li><li><ul><li>Arabic LLMs (high priority)
</li><li>Government or enterprise AI projects
</li></ul></li><li>Certifications:
</li><li><ul><li>Azure AI / ML
</li><li>MLOps / Cloud certifications</li></ul></li></ul></div>

LLM Engineer - Arabic Speaker

<div>We are seeking a talented and motivated AI Engineer with expertise in Large Language Models
(LLMs), Natural Language Processing (NLP), and Speech-to-Text technologies.
As part of our dynamic team, you will design, develop, and deploy next-generation AI solutions
that enhance our products and services through intelligent automation, language
understanding, and seamless communication systems.
Key Responsibilities
LLM Development &amp; Integration
 Fine-tune and deploy Large Language Models for chatbots, content generation, and
virtual assistants.
 Evaluate and optimize model performance in real-world use cases.
 Integrate LLMs into production environments ensuring scalability and reliability.
NLP System Design
 Design and implement NLP algorithms for text classification, sentiment analysis, entity
recognition, and summarization.
 Handle large text datasets for model training and validation.
 Collaborate with cross-functional teams to address language-related challenges.
Speech-to-Text Implementation
Develop and optimize speech-to-text (ASR) pipelines for multiple languages and
dialects.
 Integrate speech recognition systems with NLP and LLM modules for end-to-end
conversational experiences.
 Stay updated with advancements in Automatic Speech Recognition (ASR) technologies.
Performance Optimization
Improve AI model efficiency for real-time performance and scalability.
 Identify and mitigate biases to ensure model accuracy, fairness, and robustness.
Research &amp; Innovation
Stay current with cutting-edge research in LLMs, NLP, and Speech AI.
 Experiment with new architectures and techniques to drive innovation.
Documentation &amp; Collaboration
 Maintain detailed documentation of models, datasets, and workflows.
 Collaborate with product managers, software engineers, and other stakeholders to
deliver production-grade AI solutions. 
 
 
Requirements 
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Artificial Intelligence, Data Science,
or related fields.
 Proven experience in LLM development (e.g., OpenAI GPT, Claude, or similar
frameworks).
 Strong knowledge of NLP libraries (e.g., Hugging Face, spaCy, NLTK).
 Hands-on experience with speech-to-text tools (e.g., Whisper, Google Speech API,
DeepSpeech).
 Proficiency in Python and frameworks like TensorFlow or PyTorch.
 Excellent analytical, problem-solving, and communication skills.</div>

Technical Lead

<div>We are seeking a talented and motivated AI Engineer with expertise in Large Language Models (LLMs), Natural Language Processing (NLP), and Speech-to-Text technologies. 
As part of our dynamic team, you will design, develop, and deploy next-generation AI solutions that enhance our products and services through intelligent automation, language understanding, and seamless communication systems.
Key Responsibilities
LLM Development &amp; Integration<ul><li>
Fine-tune and deploy Large Language Models for chatbots, content generation, and virtual assistants.</li><li>
Evaluate and optimize model performance in real-world use cases.</li><li>
Integrate LLMs into production environments ensuring scalability and reliability.</li></ul>
NLP System Design<ul><li>
Design and implement NLP algorithms for text classification, sentiment analysis, entity recognition, and summarization.</li><li>
Handle large text datasets for model training and validation.</li><li>
Collaborate with cross-functional teams to address language-related challenges.</li></ul>
Speech-to-Text Implementation<ul><li>
Develop and optimize speech-to-text (ASR) pipelines for multiple languages and dialects.</li><li>
Integrate speech recognition systems with NLP and LLM modules for end-to-end conversational experiences.</li><li>
Stay updated with advancements in Automatic Speech Recognition (ASR) technologies.</li></ul>
Performance Optimization<ul><li>
Improve AI model efficiency for real-time performance and scalability.</li><li>
Identify and mitigate biases to ensure model accuracy, fairness, and robustness.</li></ul>
Research &amp; Innovation<ul><li>
Stay current with cutting-edge research in LLMs, NLP, and Speech AI.</li><li>
Experiment with new architectures and techniques to drive innovation.</li></ul>
Documentation &amp; Collaboration<ul><li>
Maintain detailed documentation of models, datasets, and workflows.</li><li>
Collaborate with product managers, software engineers, and other stakeholders to deliver production-grade AI solutions.</li></ul> 
Requirements 
Qualifications<ul><li>
Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Artificial Intelligence, Data Science, or related fields.</li><li>
Proven experience in LLM development (e.g., OpenAI GPT, Claude, or similar frameworks).</li><li>
Strong knowledge of NLP libraries (e.g., Hugging Face, spaCy, NLTK).</li><li>
Hands-on experience with speech-to-text tools (e.g., Whisper, Google Speech API, DeepSpeech).</li><li>
Proficiency in Python and frameworks like TensorFlow or PyTorch.</li><li>
Excellent analytical, problem-solving, and communication skills.</li></ul></div>

AI Engineer (Mid-Level)

Qualcomm

<div><h2 class="jobSectionHeader">Company:
</h2>Qualcomm Middle East Information Technology Company LLC
<h2 class="jobSectionHeader">Job Area:
</h2>Engineering Group, Engineering Group &gt; Software Test Engineering
General Summary:
About Us
<div>Qualcomm is growing its presence in Riyadh and is hiring Data Centre Engineers to support our expanding infrastructure across the region . As Saudi Arabia accelerates its digital transformation under Vision 2030, Qualcomm is investing in world‑class computing and data centre capabilities to power AI, cloud, and advanced connectivity at scale. This is a unique opportunity to work in a fast‑growing technology hub, supporting critical environments and helping shape the future of data centre operations in the Kingdom and beyond.
</div>About the Role
We are looking for a Site Reliability Engineer or Senior Engineer &ndash; Datacentre AI Engineering at Qualcomm Technologies, Inc., located in Riyadh, Saudi Arabia.
The role focuses on the design, operation, and continuous improvement of large‑scale AI inference systems in a datacenter environment. The engineer will support critical AI use cases by ensuring Qualcomm&rsquo;s AI infrastructure is reliable, scalable, and production‑ready for advanced machine‑learning workloads.
The role requires strong systems and software engineering fundamentals, hands‑on execution, and the ability to work independently on complex problem areas while collaborating closely with cross‑functional teams across hardware, software, and machine learning.
Ideal candidates will have 2-8 years of experience
Key Responsibilities will include
Key Responsibilities
1. AI Infrastructure
<ul><li>Design, deploy, and operate large‑scale AI inference systems supporting critical AI workloads.
</li><li>Ensure reliability, availability, and scalability of Qualcomm datacenter AI clusters.
</li><li>Develop and maintain software tools and support infrastructure around AI software stacks.
</li></ul>2. AI &amp; ML Engineering
<ul><li>Analyze software requirements and collaborate with architecture and hardware engineers to support AI workloads.
</li><li>Build, deploy, and operate components supporting LLM inference, agentic AI workflows, and AI services.
</li><li>Work with models, systems, and software teams to improve model performance on AI100 deployments.
</li><li>Identify and implement optimizations for workloads running on multi‑SoC and multi‑card systems.
</li></ul>3. Site Reliability Engineering (SRE)
<ul><li>Apply SRE fundamentals including monitoring, alerting, incident response, and performance optimization.
</li><li>Support production ML systems using MLOps tools and operational best practices.
</li><li>Contribute to incident reviews, operational documentation, and continuous reliability improvements.
</li></ul>4. Observability &amp; Tooling
<ul><li>Build and maintain observability tools, dashboards, and alerts to monitor system health and reliability.
</li><li>Monitor infrastructure and services using tools such as Prometheus, Grafana, CloudWatch, and custom telemetry.
</li><li>Create and maintain technical documentation, runbooks, and knowledge‑base articles.
</li></ul>5. Automation &amp; CI/CD
<ul><li>Develop automation to reduce manual operational tasks and improve system reliability.
</li><li>Support CI/CD pipelines for AI service and agent deployment.
</li><li>Apply Infrastructure‑as‑Code practices using tools such as Terraform and Ansible.
</li></ul><div></div>Required Skillset
1. AI &amp; Deep Learning
<ul><li>Experience working with AI/ML workloads such as LLMs, NLP, Vision, Audio, or Recommendation systems.
</li><li>Understand ML inference concepts including batching, token streaming, and performance considerations.
</li><li>Hands‑on experience with PyTorch and familiarity with modern ML frameworks.
</li><li>Familiarity with distributed inference, checkpointing, and accelerator‑based compute environments.
</li></ul>2. AI Operations
<ul><li>Experience supporting AI or ML applications in production environments.
</li><li>Familiarity with LLM inference pipelines and AI service operations.
</li></ul>3. Programming &amp; Software Design
<ul><li>Strong programming skills in Python with experience building and supporting production systems.
</li><li>Experience with scripting and automation using Python and Bash.
</li><li>Familiarity with configuration management and orchestration tools.
</li></ul>4. Systems &amp; Infrastructure
<ul><li>Strong Linux fundamentals include shell, containers, system services, and networking basics (DNS, TLS, HTTP/gRPC).
</li><li>Experience working with cluster schedulers such as Slurm or equivalent systems.
</li><li>Experience operating distributed systems with high availability and fault tolerance.
</li></ul>5. Observability &amp; Monitoring
<ul><li>Hands‑on experience with monitoring and logging tools such as Prometheus, Grafana, ELK, or Loki.
</li><li>Understanding of incident management, service health metrics, and system reliability monitoring.
</li></ul>6. DevOps &amp; SRE Practices
<ul><li>Solid understanding of SDLC, release processes, and operational reliability practices.
</li><li>Familiarity with CI/CD pipelines and Infrastructure‑as‑Code tools.
</li></ul><div></div>Preferred Skillset
<ul><li>Experience with GenAI, Agentic AI systems, or LLM orchestration frameworks.
</li><li>Exposure to LangChain, AutoGen, or RAG‑based systems.
</li><li>Experience with additional ML frameworks such as TensorFlow, JAX, or Ray.
</li><li>Knowledge of GPU/accelerator‑based systems and high‑performance networking (RDMA, InfiniBand, RoCE).
</li><li>Experience with advanced MLOps workflows or large‑scale AI platform operations.
</li></ul><div></div>Qualifications
<ul><li>Bachelor&rsquo;s or Master&rsquo;s degree in engineering, Computer Science, AI/ML, or a related field.
</li><li>2&ndash;8 years of software, systems, or infrastructure engineering experience, preferably in production or datacenter environments.
</li></ul>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>

Infrastructure & Site Reliability Engineer – Datacentre AI Engineering - Riyadh, KSA

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