technical support engineer

ai engineer, light

ai engineer - remote

application engineer

contracts engineer

ai prompt engineer

ClearGrid

<div>
 
 About ClearGrid
 
 
 
 ClearGrid is on a mission to
 
 revolutionize the debt resolution industry
 
 . We&rsquo;re using
 
 AI, automation, and real-time data
 
 to completely rethink how debt collection works, unlocking better outcomes for institutions and individuals alike. Think of us as rebuilding the collections &mdash;starting from scratch, with modern tools.
 
 
 We're a fast-growing startup with ambitious goals, and we&rsquo;re looking for builders who are hungry to make a real dent in a space with massive untapped potential.
 
 
 
 About The Role
 
 
 
 This is a hands-on, technical role that sits at the intersection of AI, language, and financial services. You will design and own AI agents that handle real customer conversations in Arabic and English, operating under strict compliance requirements set by our lender partners. The role demands equal parts linguistic precision, technical understanding of how LLMs work, and deep familiarity with financial services norms.
 
 
 
 What You&rsquo;ll Do
 
 
 
 
 Agent Design &amp; Prompt Engineering
 
 
 
 <ul>
 <li>
 Write and own system instructions for AI voice agents end-to-end: persona definition, tone calibration, identity verification scripts, and dialect-specific behaviour for Gulf Arabic audiences.
 </li>
 <li>
 Manage agents across the full customer contact lifecycle, from early-stage reminder agents through to payment arrangement, broken promise, and compliance-focused agents.
 </li>
 <li>
 Encode lender-specific rules directly into system instructions.
 </li>
 <li>
 Inject dynamic customer and account variables provided by the data team to personalise each agent interaction.
 
 
 </li>
 </ul>
 
 Daily Testing &amp; Quality Assurance
 
 
 
 <ul>
 <li>
 Run daily test calls to address a wide range of real-world scenarios.
 </li>
 <li>
 Listen to live and recorded calls to identify bugs, off-script responses, or compliance gaps, and ship hotfixes quickly.
 </li>
 <li>
 Work within a structured QA feedback loop, acting on review comments to improve agent quality before deployment.
 </li>
 <li>
 Coordinate with cross-functional teams to align on call triggers, variable feeds, and product rules.
 
 
 </li>
 </ul>
 
 Voice &amp; Speech Optimisation
 
 
 
 <ul>
 <li>
 Develop new agent voices and evaluate them for naturalness, clarity, and suitability per lender and dialect.
 </li>
 <li>
 Tune STT/ASR (speech-to-text), TTS (text-to-speech), and latency parameters to maintain call quality.
 </li>
 <li>
 Control temperature and model parameters to keep agents within compliant, predictable behaviour, preventing hallucination and manipulation attempts.
 
 
 </li>
 </ul>
 
 Continuous Improvement
 
 
 
 <ul>
 <li>
 Monitor developments in AI tooling, voice technology, and LLM capabilities and assess their relevance to the agent stack.
 </li>
 <li>
 Study real customer call dynamics alongside the operations team and use those insights to improve agent realism and effectiveness.
 </li>
 <li>
 Maintain detailed documentation for each prompt version across all client projects.
 
 
 </li>
 </ul>
 
 Required
 
 
 
 
 What We&rsquo;re Looking For
 
 
 
 <ul>
 <li>
 Native or near-native Arabic speakers.
 </li>
 <li>
 Demonstrated experience in AI prompt engineering or training AI/LLM models in a professional setting.
 </li>
 <li>
 Working knowledge of voice AI stack: STT/ASR, TTS, latency management, hallucination mitigation.
 </li>
 <li>
 Understanding of how LLMs think - temperature, constraints, failure modes, and manipulation resistance.
 </li>
 <li>
 Experience or background in banking, debt collection, fintech, or financial services.
 </li>
 <li>
 Ability to produce structured, unambiguous written prompts in both English and Arabic.
 </li>
 <li>
 High attention to detail and comfort with repetitive testing and incremental iteration.
 
 
 </li>
 </ul>
 
 Nice to Have
 
 
 
 <ul>
 <li>
 Hands-on experience with voice cloning tools and multi-voice agent pipelines.
 </li>
 <li>
 Familiarity with debt collection workflows: bucket segmentation, PTP cycles, RPC scripts, and lender compliance requirements.
 </li>
 <li>
 Prior exposure to Gulf-based financial institutions or BNPL platforms.
 </li>
 <li>
 Experience coordinating with QA teams and managing structured feedback loops.
 </li>
 </ul>
</div>

AI Prompt Engineer

Decima International

<div>
 Decima International is a professional services company, providing project management and controls services to clients in the United States and the Middle East. Our mission is to become the premier professional services provider across all industry sectors we are engaged in. To accomplish this, Decima International's founders established a simple yet powerful formula:
 
 
 Approach each client engagement as a
 
 partnership
 
 , create an environment that attracts the
 
 most talented professionals
 
 and allows them to flourish, and regulate
 
 growth
 
 to maintain
 
 excellence
 
 in our service standards.
 
 
 
 Decima International
 
 is a professional services company, providing project management and controls services to clients in the United States and the Middle East. Our mission is to become the premier professional services provider across all industry sectors we are engaged in. To accomplish this, Decima International's founders established a simple yet powerful formula:
 
 
 Approach each client engagement as a partnership, create an environment that attracts the most talented professionals and allows them to flourish, and regulate growth to maintain excellence in our service standards.
 
 
 We are looking for a driven and capable
 
 Mid-Level AI Engineer/Agentic Systems Developer
 
 to support the development of internal technology products. This role will write, configure, test, and maintain a growing library of autonomous AI agents designed to automate complex business workflows.
 
 
 Working under technical and product leadership, the successful candidate will translate functional workflow specifications into individual agents, tool integrations, data connections, and reliable automated processes. The ideal candidate has strong backend development capabilities and practical experience working with large language models, agentic frameworks, APIs, and retrieval-augmented generation pipelines.
 
 
 This is a highly hands-on development role focused on clean code execution, secure systems integration, continuous testing, effective debugging, and rapid iteration.
 
 
 We are looking to bring in dedicated individuals with a passion for innovation and talent for multitasking in a fast-paced environment. We will help you grow, pursue, and fulfill what inspires you so we can make big impacts on the world, together.
 
 
 
 RESPONSIBILITIES
 
 
 
 <ul>
 <li>
 Build and configure autonomous agent roles, task nodes, workflows, and conditional edges using frameworks such as LangGraph, CrewAI, and LlamaIndex.
 </li>
 <li>
 Integrate AI agents securely with internal databases and third-party platforms, including Slack, HubSpot, Salesforce, and Google Workspace, through APIs and function calling.
 </li>
 <li>
 Develop and refine retrieval-augmented generation pipelines, including data ingestion, text chunking, embedding generation, and vector database population.
 </li>
 <li>
 Diagnose and resolve agent failures involving context loss, malformed structured or JSON outputs, and incomplete multi-step tasks.
 </li>
 <li>
 Write automated unit tests for custom tools and monitor execution traces to identify and resolve edge-case failures.
 </li>
 <li>
 Maintain clean, reliable, and well-structured code while supporting continuous testing and rapid product iteration.
 
 
 </li>
 </ul>
 
 QUALIFICATIONS
 
 
 
 
 Required Qualifications:
 
 
 
 <ul>
 <li>
 Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Information Technology, or related fields.
 </li>
 <li>
 
 2 to 4 years
 
 of professional experience in backend software development.
 </li>
 <li>
 
 At least 1 year
 
 of practical experience working directly with large language model APIs and prompt construction.
 </li>
 <li>
 Strong familiarity with agentic AI frameworks such as LangGraph, CrewAI, or LlamaIndex.
 </li>
 <li>
 Solid understanding of structural prompt design, including generating reliable JSON and other structured outputs.
 </li>
 <li>
 High proficiency in Python, including a practical understanding of asynchronous programming concepts.
 </li>
 <li>
 Experience developing and integrating RESTful APIs using FastAPI, Flask, or similar frameworks.
 </li>
 <li>
 Proficiency in version control and collaborative software development using Git.
 </li>
 <li>
 Working knowledge of relational databases, preferably PostgreSQL.
 </li>
 <li>
 Foundational experience using vector databases or stores such as Chroma or Pinecone for data retrieval.
 </li>
 <li>
 Experience integrating software applications with databases, APIs, and third-party platforms.
 </li>
 <li>
 Strong debugging, analytical, testing, documentation, and problem-solving skills.
 </li>
 <li>
 Ability to translate functional workflow requirements into reliable technical implementations.
 </li>
 <li>
 Ability to work independently, manage development priorities, and iterate quickly in a fast-paced environment.
 
 
 </li>
 </ul>
 
 Preferred Qualifications:
 
 
 
 <ul>
 <li>
 Experience developing autonomous agents or multi-agent workflows for practical business use cases.
 </li>
 <li>
 Experience implementing retrieval-augmented generation applications and data-ingestion pipelines.
 </li>
 <li>
 Experience with secure function calling and integrations involving business platforms such as Slack, HubSpot, Salesforce, or Google Workspace.
 </li>
 <li>
 Experience testing, tracing, and monitoring multi-step AI workflows in development or production environments.
 </li>
 <li>
 Familiarity with prompt evaluation, structured-output validation, and common large language model failure patterns.
 </li>
 <li>
 Experience contributing to the development of internal technology products or workflow-automation solutions.
 
 
 </li>
 </ul>
 
 POSITION DETAILS
 
 
 
 <ul>
 <li>
 
 Primary Location (Office-based):
 
 Heliopolis, Cairo, Egypt
 </li>
 <li>
 
 Position:
 
 Mid-Level AI Engineer/Agentic Systems Developer
 </li>
 <li>
 
 Position Classification:
 
 Salary-based full-time hours
 
 
 </li>
 </ul>
 
 PRODUCTIVITY TOOLS
 
 
 
 <ul>
 <li>
 Python
 </li>
 <li>
 LangGraph
 </li>
 <li>
 CrewAI
 </li>
 <li>
 LlamaIndex
 </li>
 <li>
 FastAPI
 </li>
 <li>
 Flask
 </li>
 <li>
 Git
 </li>
 <li>
 PostgreSQL
 </li>
 <li>
 Chroma
 </li>
 <li>
 Pinecone
 </li>
 <li>
 Microsoft Office
 </li>
 <li>
 Microsoft 365
 
 
 </li>
 </ul>
 Decima International is an Equal Opportunity Employer that is committed to the safety and well-being of all. All qualified applicants will receive consideration for employment without regard to race, religion, creed, color, national origin, ancestry, sex (including pregnancy, childbirth, breastfeeding, or medical conditions related to pregnancy, childbirth, or breastfeeding), age, medical condition, marital or domestic partner status, sexual orientation, gender, gender identity, gender expression and transgender status, mental disability or physical disability, genetic information, military or veteran status, citizenship, low-income status or any other status or characteristic protected by applicable law.
</div>

Mid-Level AI Engineer/Agentic Systems Developer (On-site) - Cairo, Egypt

Coca-Cola HBC

<div>
 
 
 Department:
 
 Data Insights &amp; Artificial Intelligence, Digital &amp; Technology Platform Services
 
 
 
 
 
 We are seeking a visionary Senior Knowledge Graph &amp; Context Engineer for the end-to-end design, build, deployment, and continuous improvement of the knowledge and context capabilities that power our AI assistants along with our enterprise search, and decision-support solutions. You will harness the context layer creation that will connect enterprise data, business knowledge, semantic models, and AI services, ensuring that product, ingredient, regulatory, quality, sustainability, marketing, and supply chain knowledge is delivered accurately, safely, and at speed through reusable and scalable solutions. The ideal candidate will be part of our enterprise insights and transformation agenda, helping translate fragmented knowledge assets across source systems, such as ERP , CRM , PLM, PIM/MDM, LIMS, Regulatory, DAM, and SCM, into trusted, reusable, and governed context packs for Agentic AI use cases. The position requires a blend of business understanding, semantic / knowledge engineering capability, and AI delivery expertise, with a strong focus on enabling high-quality grounding, retrieval, governance, and adoption across the organization.
 
 
 
 
 
 
 YOUR NEW KEY RESPONSIBILITIES:
 
 
 <ul>
 <li>
 Design, build, and operationalize the enterprise context layer that supports AI assistants, semantic search, and decision-support solutions across business domains.
 </li>
 <li>
 Transform enterprise knowledge assets into reusable, trusted, and scalable context packs for Agentic AI outcomes.
 </li>
 <li>
 Design and optimize retrieval pipelines, grounding patterns, chunking strategies, metadata enrichment, and prompt/context orchestration to ensure accurate, relevant, and safe AI responses.
 </li>
 <li>
 Partner closely with Ontology Engineers, AI Engineers, and Data teams to apply semantic enrichment, taxonomies, controlled vocabularies, and relationship modelling that improve discoverability, interoperability, and reasoning.
 </li>
 <li>
 Embed policy-aware controls, access rules, governance standards, and quality checks into context pipelines to ensure compliant and trusted use of enterprise knowledge.
 </li>
 <li>
 Work with Data Platform and Engineering teams to productionize context services across graph, search, and AI ecosystems, ensuring performance, scalability, monitoring, and maintainability.
 </li>
 <li>
 Partner with Product, Data Platform, Security / Compliance, Architecture, and business domain teams to align priorities with business needs and accelerate adoption of AI-enabled knowledge solutions.
 </li>
 <li>
 Help improve findability, reuse, decision quality, and business efficiency, while tracking impact, adoption, and value realization.
 </li>
 <li>
 Support the embedding of knowledge-driven and AI-enabled processes into business workflows, tools, and digital products.
 </li>
 <li>
 Drive continuous improvement in knowledge quality, retrieval effectiveness, semantic consistency, and AI response performance.
 </li>
 </ul>
 
 
 
 
 
 ARE THESE YOUR SECRET INGREDIENTS?
 
 
 <ul>
 <li>
 BSc or MSc in a relevant field such as Computer Science, Information Science, Data Science, Artificial Intelligence, Knowledge Engineering, Computational Linguistics, or a related discipline.
 </li>
 <li>
 1+ experience in knowledge engineering, context engineering, semantic technologies, enterprise search, GenAI / LLM solutions, or AI product enablement.
 </li>
 <li>
 Strong understanding of one or more of the following: ontologies, taxonomies, controlled vocabularies, knowledge graphs, metadata modelling, semantic enrichment, and information retrieval.
 </li>
 <li>
 Hands-on experience designing or supporting RAG pipelines, prompt / grounding patterns, context orchestration and memory management, context quality and provenance, semantic search, and evaluation of AI responses.
 </li>
 <li>
 Experience with relevant technologies such as graph databases, vector / search platforms, semantic web technologies, and cloud AI / data ecosystems. Exposure to tools and W3C standards such as RDF, OWL, SKOS, SHACL, SPARQL, Neo4j, Stardog, Ontotext GraphDB, Azure ecosystem, or equivalent, is considered a strong advantage.
 </li>
 <li>
 Experience working with enterprise knowledge and data domains such as PLM, PIM / MDM, LIMS, Regulatory, DAM, ERP, SCM, or other structured and unstructured business data sources.
 </li>
 <li>
 Strong influencing, interpersonal, and communication skills, with the ability to work effectively across Data, AI, Technology, and business functions.
 </li>
 <li>
 Experience presenting solutions, trade-offs, and recommendations to senior management and technical leadership teams.
 </li>
 </ul>
 
 
 
 
 
 ABOUT YOUR NEW TEAM:
 
 
 
 We are Coca-Cola Hellenic, a growth-focused consumer goods business and strategic bottling partner of the Coca-Cola Company. We bottle, distribute and sell an unrivalled range of products in 29 markets in Europe, Africa and Eurasia. As we do, we create value for all stakeholders, support socio-economic growth and build a more positive environmental impact.
 
 
 We bring together more than 30,000 people from over 70 nationalities, coming from five continents. The diversity of our markets, from mature to emerging economies, provides a wide range of attractive opportunities for growth.
 
 
 We nurture our talents. We give opportunities to people across all functions and levels, as well as different geographies, backgrounds and education. We are willing to take a risk on the people we believe in, even if they don&rsquo;t have the perfect experience. We have faith in what every person can be.
 
 
 
 
 
 
 AT COCA-COLA HBC, DIVERSITY HELPS US THRIVE
 
 
 
 At Coca-Cola HBC, we are an inclusive employer that thrives on diversity. This means our environment provides equal opportunities for all, regardless of race, color, religion, age, disability, sexual orientation, or gender identity. Join us in nurturing a culture where everyone belongs and contributes to our collective success.
 
</div>

Senior Engineer – Knowledge Graph & Context

Siemens

<div>
 
 About Brightly
 
 
 
 Brightly is a global leader in intelligent, cloud‑based asset lifecycle management, empowering 12,000+ clients with predictive insights to optimize maintenance, energy, and capital planning&mdash;backed by Siemens. Our products help schools, hospitals, cities, and manufacturers keep critical infrastructure running efficiently and sustainably.
 
 
 
 The Opportunity
 
 
 
 We&rsquo;re looking for a Senior Machine Learning Engineer to lead LLM‑powered application development&mdash;from prototype to production&mdash;on AWS. You&rsquo;ll design robust ML/LLM services that power search, recommendations, copilots, and workflow automation in Brightly&rsquo;s platform, partnering closely with product, data, and engineering teams. Responsibilities and skill expectations reflect current industry practice for senior ML/LLM engineers, including end‑to‑end model lifecycle ownership, production‑grade code, and MLOps.
 
 
 
 What You&rsquo;ll Do
 
 
 
 <ul>
 <li>
 Build LLM applications: Design and implement RAG pipelines, prompt orchestration, tools/agents, safety/guardrails, and evaluation harnesses; instrument for latency, cost, and quality. (Guided by current LLM engineer role practices.)
 </li>
 <li>
 Own the ML lifecycle: Data curation, feature engineering, training/fine‑tuning (LoRA/QLoRA), A/B testing, deployment, monitoring, and continuous improvement of models and prompts.
 </li>
 <li>
 Productionize on AWS: Ship scalable services on EKS/ECS/Lambda; leverage SageMaker, Bedrock, EMR, MSK, Step Functions; apply observability (CloudWatch/OpenTelemetry) and cost controls. (Duties aligned to modern AWS ML roles.)
 </li>
 <li>
 Scale training &amp; inference: Use distributed training (FSDP/DeepSpeed), quantization, caching, vector databases, and GPU/Inferentia for performance and efficiency.
 </li>
 <li>
 MLOps &amp; governance: Establish CI/CD for models (MLflow/Kedro/SageMaker Pipelines), model/version registries, data and prompt lineage, evaluation gates, and responsible‑AI controls. (Aligned with contemporary MLOps templates.)
 </li>
 <li>
 Partner across Brightly: Translate asset‑management use cases into ML/LLM solutions; collaborate with product managers and UX to ship customer‑visible features that measurably improve reliability, safety, and sustainability.
 </li>
 <li>
 Mentor &amp; lead: Provide technical leadership, review designs/PRs, and raise the bar on ML engineering excellence across the team. (Common senior ML expectations.)
 </li>
 <li>
 Perform Exploratory Data Analysis (EDA) on structured, semi‑structured, and unstructured datasets to identify patterns, correlations, feature importance, and data quality issues.
 
 
 </li>
 </ul>
 (Consistent with ML engineer responsibilities to analyze data before model development.)
 
 
 <ul>
 <li>
 Conduct deep research on asset-related, operational, and domain-specific datasets to understand root causes, trends, and predictive signals.
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Bring
 
 
 
 
 Required experience
 
 
 
 <ul>
 <li>
 5&ndash;7 years total software/ML engineering experience, with 3+ years building and operating ML systems in production.
 </li>
 <li>
 1+ years hands‑on LLM application development (e.g., RAG, fine‑tuning, prompt engineering, evaluators/guardrails, agentic workflows) using packages such as Langchain and Langgraph.
 </li>
 <li>
 AWS proficiency (3+ years): Strong with core services (EKS/ECS, Lambda, S3, DynamoDB/RDS, Step Functions, IAM) and ML stack (SageMaker, Bedrock or HF on AWS). (Representative AWS ML role skills.)
 </li>
 <li>
 Modeling &amp; frameworks: Python, PyTorch, Hugging Face ecosystem; vector stores (e.g., OpenSearch, PGVector, Pinecone), embeddings, retrieval, and evaluation metrics for NLP/LLMs. (In line with senior LLM roles.)
 </li>
 <li>
 MLOps: CI/CD for ML, model registries, experiment tracking, telemetry/monitoring, automated retraining; Docker/Kubernetes, GitHub Actions/GitLab CI. (Current MLOps expectations.)
 </li>
 <li>
 Data engineering fluency: ETL/ELT, streaming/batch (Spark/Flink), data quality and governance controls for ML.
 
 
 </li>
 </ul>
 
 Nice to have
 
 
 
 <ul>
 <li>
 Experience with distributed training (FSDP, DeepSpeed), RLHF, or Inferentia/Trainium optimization.
 </li>
 <li>
 Exposure to sustainability/asset/intelligent operations domains.
 </li>
 <li>
 Familiarity with security &amp; compliance for ML systems in enterprise environments. (Frequently included in senior ML roles.)
 
 
 </li>
 </ul>
 How you&rsquo;ll work
 
 
 <ul>
 <li>
 Pragmatic and product‑oriented: You bias to measurable outcomes and iterate quickly with stakeholders. (Modern senior ML role framing.)
 </li>
 <li>
 Engineering excellence: You write production‑quality Python, design reliable APIs/services, and uphold testing/observability standards. (Common duties in senior templates.)
 </li>
 <li>
 Collaborative leadership: You mentor peers and influence architecture across teams. (Industry‑standard senior expectations.)
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s in CS/EE/Math or related field (Master&rsquo;s preferred) or equivalent practical experience. (Typical for senior ML roles.)
 
 
 </li>
 </ul>
 Join a mission‑driven team building technology that keeps communities running&mdash;safer, greener, and more resilient&mdash;at global scale. You&rsquo;ll pair startup‑speed product work with the reach and rigor of Siemens.
 
 
 
 The Brightly culture
 
 
 
 We&rsquo;re guided by a vision of community that serves the ambitions and wellbeing of all people, and our professional communities are no exception. We model that ideal every day by being supportive, collaborative partners to one another, conscientiously making space for our colleagues to grow and thrive. Our passionate team is driven to create a future where smarter infrastructure protects the environments that shape and connect us all. That brighter future starts with us.
</div>

Senior Machine Learning Engineer

Procore Technologies

<div>
 
 Job Ad
 
 
 
 We&rsquo;re looking for a Software Engineer - Agentic Foundation to join Procore&rsquo;s Product &amp; Technology Team. The Agentic Foundation team treats AI adoption as an internal product, building and operating the tooling, frameworks, and services that make AI-native workflows the default across Procore's software development lifecycle. This position requires developer productivity experience to achieve high velocity without compromising on quality in this critical functional domain.
 
 
 Procore software solutions aim to improve the lives of everyone in construction and the people within Product &amp; Technology are the driving force behind our innovative, top-rated global platform. We&rsquo;re a customer-centric group that encompasses engineering, product, product design and data, security and business systems.
 
 
 We&rsquo;re looking for someone to join us immediately.
 
 
 
 
 What you&rsquo;ll do:
 
 
 
 
 <ul>
 <li>
 AI is central to how we work. You&rsquo;ll be expected to master generative tools and agentic workflows, moving beyond just using AI to actively building the agentic future of construction.
 </li>
 <li>
 Design, build, and evolve the core agentic tooling and frameworks that enable Procore engineers to leverage AI natively across the software development lifecycle &mdash; from code generation and review to test orchestration and deployment.
 </li>
 <li>
 Conduct thorough, constructive code reviews and pair with teammates to elevate code quality and knowledge-sharing across the team.
 </li>
 <li>
 Collaborate with infrastructure, platform, and product engineering teams to align on shared standards and avoid duplicated effort.
 </li>
 <li>
 Establish and champion engineering standards, best practices, and platform contracts that allow hundreds of development teams to ship safely at high velocity.
 </li>
 <li>
 Focus on metrics and observability: We intend to become an elite performer in the software industry, and we can only do this if we learn and adapt quickly. We need telemetry from our agentic software delivery systems from which to continually learn how to improve our lead time for changes, our change failure rate, and our mean time to recover from failure.
 
 
 </li>
 </ul>
 
 
 What we&rsquo;re looking for:
 
 
 
 
 <ul>
 <li>
 3+ years of professional software engineering experience, with experience in full-stack development and backend systems.
 </li>
 <li>
 Strong fundamentals in at least one of: Ruby on Rails, Python or TypeScript; comfort reading and contributing across multiple languages.
 </li>
 <li>
 Solid DevOps instincts: you understand deployment strategies, rollback mechanisms, feature flags, and what 'safe to deploy' actually means in a high-scale environment.
 </li>
 <li>
 Hands-on experience with AI coding tools (Claude, Cursor, Copilot, or similar) and building or customizing AI-assisted workflows: skills, prompt engineering, MCP integrations, evals, or agentic pipelines.
 </li>
 <li>
 Experience working with modern web development including development, testing, staging, deploying, and monitoring
 </li>
 <li>
 Comfortable exploring uncharted territory: AI tooling is a fast-moving space with no established playbook. You evaluate new technologies critically, form your own conclusions from experimentation, and can distinguish what actually works from what's just trending.
 
 
 </li>
 </ul>
 
 
 Nice to Have
 
 
 
 
 <ul>
 <li>
 Experience building or integrating AI-native developer tooling &mdash; LLM-assisted code review, agentic test generation, AI-powered incident triage, or similar.
 </li>
 </ul>
</div>

Software Engineer 2, Agentic Foundation

Unifonic

<div>
 
 Job Description
 
 
 
 Proudly voted a Great Place to Work&reg;, we are a dynamic startup in the SaaS space that is revolutionizing the way businesses communicate. Our team is made up of 500 energetic and passionate Unifones who are dedicated to delivering the best possible experience to 5000+ customer-centric companies.
 
 
 We pride ourselves on our fun and collaborative work environment, where creativity and new ideas are constantly encouraged. As shareholders in the business, we&rsquo;re so much more than a group of passionate communicators. We are Unifones. Join our team and be a part of something big!
 
 
 
 Meet the team!
 
 
 
 Our Engineering team is responsible for designing, developing, and maintaining the systems and technologies that drive Unifonic&rsquo;s solutions. We work closely with other departments to ensure our products and services meet the needs of our customers. If you are passionate about technology and are excited about working on cutting-edge communication and engagement solutions, we want you on our team.
 
 
 As a Senior Machine Learning (AI) Engineer, you will be responsible for designing, developing, and deploying advanced machine learning solutions across various domains, including NLP, Text Classification, RAG, LLMs, Recommender engines, and Anomaly detection. This role involves end-to-end project ownership, from data preprocessing to the creation of service APIs, and offers opportunities to work on cutting-edge AI technologies.
 
 
 
 Help Us Shape The Future Of Communication By
 
 
 
 <ul>
 <li>
 Leading the end-to-end design, development, and deployment of robust and scalable machine learning solutions, with a strong emphasis on NLP and RAG architectures.
 </li>
 <li>
 Architecting and implementing RAG systems, combining large language models (LLMs) with robust retrieval mechanisms to improve the accuracy, factual grounding, and interpretability of generated content.
 </li>
 <li>
 Applying advanced NLP techniques for tasks such as text classification, entity recognition, sentiment analysis, summarization, question answering, and information extraction.
 </li>
 <li>
 Researching, evaluating, and integrating state-of-the-art NLP models and RAG frameworks (e.g., Transformers, BERT, GPT variants, Vector Databases, Semantic Search).
 </li>
 <li>
 Mentoring junior team members on the team, sharing knowledge, and advising the best machine learning and software engineering practices and approaches.
 </li>
 <li>
 Establishing and maintaining robust communication channels with other cross-functional teams to facilitate the integration of machine learning solutions into other Unifonic products.
 </li>
 <li>
 Developing and optimizing highly confident machine learning algorithms and models and creating/exposing the service APIs using frameworks such as Flask, FastAPIs, or other relevant frameworks.
 </li>
 <li>
 Staying up to date with the latest machine learning research papers, and AI trends (i.e. Generative AI).
 </li>
 <li>
 Collaborating with the data engineering team and other teams to collect and analyze extensive datasets, extracting insights and patterns, in real-time, near-real-time, or batch processing mode.
 </li>
 <li>
 Implementing proof of concepts and prototypes to demonstrate the potential of new AI use cases and innovations.
 </li>
 <li>
 Building scalable, maintainable machine learning services, which should handle thousands of requests per second, and help to perform the required load tests to meet the SLA.
 </li>
 <li>
 Reviewing the code of other team members and suggesting improvements to ensure the SOLID principles and clean architecture.
 </li>
 <li>
 Assisting in the project documentation and demos.
 
 
 </li>
 </ul>
 
 Job Requirements
 
 
 
 
 What you'll bring:
 
 
 
 <ul>
 <li>
 Proven experience designing and implementing RAG systems, including familiarity with various retrieval strategies (e.g., BM25, dense retrieval, hybrid approaches) and knowledge graph integration.
 </li>
 <li>
 Hands-on experience with LLM orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar tools for building and managing autonomous agents.
 </li>
 <li>
 Deep expertise in various NLP techniques and models, including but not limited to:
 
 
 </li>
 </ul>
 Transformer architectures (e.g., BERT, GPT, T5, LLama, Mistral)
 
 
 Large Language Models (LLMs) and their fine-tuning/adaptation
 
 
 Vector embeddings and similarity search
 
 
 Text classification, named entity recognition (NER), sentiment analysis, summarization, and question answering.
 
 
 <ul>
 <li>
 Hands-on 3-5 years of relevant work experience as a Machine Learning Engineer.
 </li>
 <li>
 Hands-on 3+ years of experience with Python.
 </li>
 <li>
 Excellent analytical abilities, with the capacity to collect, organize, and analyze large datasets to glean valuable insights.
 </li>
 <li>
 End-to-end experience in training, evaluating, testing, and deploying machine learning products in production.
 </li>
 <li>
 Ability to write world-class code in Python (SOLID principles), considering the best software engineering fundamentals, i.e. data structures, algorithms, and data modeling
 </li>
 <li>
 Solid experience in ML frameworks such as NumPy, Pandas, Scikit-Learn, PyTorch, Keras, BERT, Tensorflow, and similar.
 </li>
 <li>
 Familiarity with MLOps best practices, e.g. Model deployment and reproducible research.
 </li>
 <li>
 Mastering data science needed skills like SQL, hypothesis testing, Data cleansing, data augmentation, data pre-processing techniques, and dimensionality reduction.
 </li>
 <li>
 Basic knowledge of Kubernetes and Docker is nice to have.
 </li>
 <li>
 Excellent understanding of Machine learning techniques like Naive Bayes classifiers, SVM, Decision Tree, KNN, K-means, Random Forest, modeling and optimization, evaluation metrics, classification, and clustering.
 </li>
 <li>
 Experience with the Hugging Face libraries (i.e. transformers).
 </li>
 <li>
 Experience fine-tuning pre-trained models and using vector search to enhance LLMs results.
 </li>
 <li>
 Experience with LLM frameworks (i.e. LangChain) and prompt engineering techniques.
 </li>
 <li>
 Familiar with code versioning tools such as GIT, CI/CD concepts, and toolchains.
 </li>
 <li>
 Familiar with Agile methodologies i.e. scrum and kanban.
 </li>
 <li>
 Ability to develop high-level architecture and low-level design, End-to-end for a specific project.
 </li>
 <li>
 Experience in event sourcing patterns and tools i.e. Kafka, RabbitMQ, or similar is a plus.
 </li>
 <li>
 Experience with LLM frameworks (i.e. LangChain) and prompt engineering techniques is nice to have.
 </li>
 <li>
 Experience in event sourcing patterns and tools i.e. Kafka, RabbitMQ, or similar is nice to have.
 </li>
 <li>
 General knowledge of Data warehouse tools e.g. Vertica is a plus.
 </li>
 <li>
 A Bachelor&rsquo;s degree in a relevant field. (e.g. Computer Science, Computer Engineering, Software, etc).
 </li>
 <li>
 Excellent communication and collaboration skills.
 </li>
 <li>
 Good level of spoken and written Arabic and English.
 
 
 </li>
 </ul>
 
 As a Unifone you will receive a range of benefits:
 
 
 
 <ul>
 <li>
 Competitive salary and bonus.
 </li>
 <li>
 Unifonic share scheme (we are all owners!).
 </li>
 <li>
 30 holiday days after the first anniversary.
 </li>
 <li>
 Your Birthday off!
 </li>
 <li>
 Spend up to 25 days per year working from anywhere in the world!
 </li>
 <li>
 Paid leave for new parents.
 </li>
 </ul>
</div>

Velents.ai

<div>
 حلول فيلينتس بالذكاء الاصطناعي تخدم أكثر من 5 وزارات حكومية في المملكة العربية السعودية والكثير من الشركات الكبيرة والناشئة في مختلف القطاعات مثل مجالات التوظيف، التعليم، تطوير الموظفين، تأهيل الطلاب، خدمة العملاء، المبيعات، وغيرها. وذلك من خلال تطوير واجهات برمجة التطبيقات مثل المحادثات التفاعلية مع الذكاء الاصطناعي، تحويل الصوت والفيديو إلى نص، .تحليل الفيديو، قراءة ومطابقة المستندات، اختبارات المهارات، ونظام مكافحة الغش. والمزيد والمزيد.. تعرف أكثر على حلول فيلينتس بالذكاء الاصطناعي من خلال موقعنا
 
 
 About the role&nbsp;
 
 
 
 We&rsquo;re looking for a mid-level AI Engineer who lives close to the model. Your core work is training and fine-tuning foundation models across language and speech &mdash; large language models (LLMs), automatic speech recognition (ASR), and text-to-speech (TTS). Beyond training, you&rsquo;ll also build GenAI applications, integrate models into agentic workflows, and own the MLOps that keeps everything running in production. It&rsquo;s a full-stack AI role with model training at its heart.&nbsp;
 
 
 
 
 
 
 Core responsibilities &mdash; model training &amp; fine-tuning&nbsp;
 
 
 <ul>
 <li>
 Fine-tune LLMs using parameter-efficient methods (LoRA, QLoRA, PEFT, adapters) and full fine-tuning where needed.&nbsp;
 </li>
 <li>
 Train and fine-tune ASR models&nbsp;for custom vocabularies, accents, and low-resource languages.&nbsp;
 </li>
 <li>
 Develop and fine-tune TTS systems for natural prosody, voice cloning, and multilingual synthesis.&nbsp;
 </li>
 <li>
 Curate, clean, and prepare high-quality datasets &mdash; text corpora, audio transcriptions, and speech recordings.&nbsp;
 </li>
 <li>
 Run rigorous evaluation:&nbsp;task metrics for LLMs, WER/CER for ASR, MOS and naturalness for TTS.&nbsp;
 </li>
 <li>
 Apply alignment techniques (RLHF, DPO, SFT) to steer LLM behavior toward desired outcomes.&nbsp;
 
 
 </li>
 </ul>
 
 
 
 
 
 Supporting responsibilities &mdash; GenAI, agents &amp; production&nbsp;
 
 
 <ul>
 <li>
 Build LLM-powered applications including RAG pipelines, prompt chains, and conversational systems.&nbsp;
 </li>
 <li>
 Integrate trained models into agentic workflows with tool-calling, memory, and orchestration (LangChain, LangGraph).&nbsp;
 </li>
 <li>
 Optimize models for inference &mdash; quantization (INT4/INT8/GPTQ), pruning, distillation, and ONNX/TensorRT export.&nbsp;
 </li>
 <li>
 Familiarity with vllm, sglang, trt&nbsp;
 </li>
 <li>
 Own MLOps: CI/CD for models, containerized deployment, versioning, and production monitoring.&nbsp;
 </li>
 <li>
 Track model drift, latency, cost, and accuracy in production; respond to incidents and retrain as needed.&nbsp;
 </li>
 <li>
 Collaborate with product and engineering to turn business needs into shipped AI capabilities.&nbsp;
 
 
 </li>
 </ul>
 
 
 
 
 
 Requirements&nbsp;
 
 
 
 
 MUST HAVE
 
 
 &nbsp;
 
 
 <ul>
 <li>
 2&ndash;5 years hands-on training or fine-tuning deep learning models.&nbsp;
 </li>
 <li>
 Strong Python and PyTorch; comfortable modifying model source code.&nbsp;
 </li>
 <li>
 Deep experience with the HuggingFace ecosystem (Transformers, Datasets, PEFT, Accelerate).&nbsp;
 </li>
 <li>
 Solid grasp of transformer architectures and tokenization.&nbsp;
 </li>
 <li>
 Audio processing with torchaudio, librosa, soundfile.&nbsp;
 </li>
 <li>
 Experience deploying and monitoring models in cloud environments.&nbsp;
 
 
 </li>
 </ul>
 
 
 NICE TO HAVE
 
 
 &nbsp;
 
 
 <ul>
 <li>
 Low-resource or multilingual speech/language model training.&nbsp;
 </li>
 <li>
 Distributed training experience (DeepSpeed, FSDP, or Megatron-LM).&nbsp;
 </li>
 <li>
 Experience building agentic systems end-to-end.&nbsp;
 </li>
 <li>
 Speech data collection, annotation, or augmentation pipelines.&nbsp;
 </li>
 <li>
 Background in phonetics, linguistics, or acoustic modeling.&nbsp;
 
 
 </li>
 </ul>
 
 
 
 
 
 Tools &amp; stack&nbsp;
 
 
 
 Python, PyTorch, HuggingFace, DeepSpeed, FSDP, Weights &amp; Biases, MLflow, ONNX / TensorRT,Vllm,Sglang,langchain,langgraph, torchaudio, ,&nbsp;Docker, SLURM / Kubernetes, AWS / GCP, Git + DVC.&nbsp;
 
 
 
 
 
 
 Education&nbsp;
 
 
 
 Bachelor&rsquo;s or Master&rsquo;s in Computer Science, Electrical Engineering, Computational Linguistics, or a related field. A strong portfolio of model training work is equally valued.&nbsp;
 
</div>

AI Engineer — Model Training & Fine-tuning

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