llm engineer

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

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

Digitrends

AI EngineerKey Responsibilities<ul><li>Design, develop, and fine-tune LLM-based AI models for real-world applications.</li><li>Implement custom LLM training, reinforcement learning, and fine-tuning using open-source and proprietary models like GPT, Llama, Mistral, Claude, or Falcon.</li><li>Optimize model efficiency, inference speed, and resource utilization for deployment in cloud or on-premise environments.</li><li>Build data pipelines for training and evaluating AI models.</li><li>Implement prompt engineering, retrieval-augmented generation (RAG), and memory-based techniques for improved performance.</li><li>Integrate LLMs into applications using APIs, LangChain, vector databases (e.g., Pinecone, FAISS, ChromaDB).</li><li>Develop custom embeddings, transformers, and tokenization strategies for specific use cases.</li><li>Work closely with MLOps, backend engineers, and data scientists to ensure smooth deployment and monitoring of AI solutions.</li><li>Research and stay updated on emerging trends in NLP, generative AI, and model compression techniques.</li><li>Ensure compliance with ethical AI principles and data security standards when handling sensitive data.</li></ul>Required Skills:<ul><li>Strong knowledge of Python and frameworks like PyTorch, TensorFlow, JAX.</li><li>Experience with Hugging Face Transformers, OpenAI API, LangChain, and vector databases.</li><li>Understanding of LLM architectures, fine-tuning techniques (LoRA, QLoRA, PEFT), and model distillation.</li><li>Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).</li><li>Familiarity with distributed computing, GPU acceleration (CUDA, TensorRT), and multi-node training.</li><li>Knowledge of AI safety, bias mitigation, and responsible AI development.</li><li>Strong problem-solving, debugging, and optimization skills.</li><li>Excellent communication and teamwork skills.</li></ul>Preferred Qualifications<ul><li>Experience in building domain-specific AI assistants or chatbots.</li><li>Knowledge of multimodal AI, speech-to-text, text-to-image models.</li><li>Contributions to open-source AI projects or research papers in NLP/LLMs.</li></ul>Key Benefits<ul><li>Working in a fast-paced tech start-up environment</li><li>Attractive salary and incentives for high-quality work</li><li>Flexible schedule</li><li>Health Insurance</li><li>Five Working Days</li><li>Provident Fund</li></ul>Job Type: Full-timeWork Location: In person

AI Engineer

CES - MATLAB® in the Middle East

<div>
 
 Overview
 
 
 Software engineer with proven, hands-on experience developing software using Large Language Models (LLMs) and agent-based coding tools (e.g., Codex, Claude Code, or similar). This capability is mandatory and must be demonstrated.
 
 
 The role combines traditional backend/software engineering with the design and orchestration of LLM-driven development workflows. The candidate must be capable of directing agents effectively to build, extend, and maintain production-grade systems. The position is on-site.
 
 
 
 
 
 Responsibilities
 
 
 LLM-Driven Development &amp; Agent Orchestration
 
 <ul>
 <li>
 Design and implement workflows using LLM-based coding agents.
 </li>
 <li>
 Drive software development through tools such as Codex, Claude Code, or equivalent.
 </li>
 <li>
 Define prompts, constraints, and system structures to ensure reliable agent output.
 </li>
 <li>
 Evaluate, debug, and refine agent-generated code.
 </li>
 <li>
 Establish best practices for agent-assisted development across the organization.
 </li>
 </ul>
 
 Backend &amp; Service Development
 
 <ul>
 <li>
 Design, develop, and maintain internal backend services.
 </li>
 <li>
 Implement APIs and service layers supporting internal platforms and tools.
 </li>
 <li>
 Work with databases (SQL/NoSQL), ensuring performance and reliability.
 </li>
 <li>
 Handle authentication and authorization mechanisms (e.g., OAuth, JWT, identity providers).
 </li>
 </ul>
 
 Infrastructure &amp; Containerization
 
 <ul>
 <li>
 Build and manage containerized applications (Docker-based environments).
 </li>
 <li>
 Support deployment workflows and service orchestration.
 </li>
 <li>
 Ensure services are scalable, maintainable, and secure.
 </li>
 </ul>
 
 Integrations &amp; External Systems
 
 <ul>
 <li>
 Integrate with third-party APIs and external platforms.
 </li>
 <li>
 Design robust data flows between internal systems and external services.
 </li>
 <li>
 Handle failures, retries, and monitoring of integrations.
 </li>
 </ul>
 
 LLM-Based System Integration
 
 <ul>
 <li>
 Embed LLM capabilities into internal tools and services.
 </li>
 <li>
 Build agent-based systems for automation, data processing, and engineering workflows.
 </li>
 <li>
 Work on secure and controlled usage of LLMs (data handling, privacy, execution boundaries).
 </li>
 </ul>
 
 System Reliability &amp; Maintenance
 
 <ul>
 <li>
 Maintain and improve existing internal systems.
 </li>
 <li>
 Debug complex issues across services, integrations, and agent workflows.
 </li>
 <li>
 Implement logging, monitoring, and alerting where needed.
 </li>
 </ul>
 
 
 
 
 Requirements
 
 
 
 
 
 Must Have
 
 <ul>
 <li>
 3&ndash;6 years of hands-on software development experience
 </li>
 <li>
 Proven, demonstrable experience developing software using LLMs
 </li>
 <li>
 Practical experience using agent-based coding tools (e.g., OpenAI Codex, Claude Code or equivalent)
 </li>
 <li>
 Strong backend development capability (Python, Node.js, or similar)
 </li>
 <li>
 Experience designing and building APIs (REST or similar)
 </li>
 <li>
 Solid understanding of databases (SQL and/or NoSQL)
 </li>
 <li>
 Experience implementing authentication and authorization (OAuth, JWT, etc.)
 </li>
 <li>
 Hands-on experience with Docker and containerized environments
 </li>
 <li>
 Ability to validate, debug, and correct AI-generated code
 </li>
 <li>
 Strong problem-solving and debugging skills
 </li>
 <li>
 Ability to work independently and structure ambiguous problems
 </li>
 <li>
 Professional level of English
 </li>
 </ul>
 
 
 
 
 Nice to Have
 
 <ul>
 <li>
 Experience building agent-based or multi-agent systems
 </li>
 <li>
 Strong prompt engineering skills and LLM evaluation techniques
 </li>
 <li>
 Experience integrating LLMs into production systems
 </li>
 <li>
 Familiarity with CI/CD pipelines and DevOps practices
 </li>
 <li>
 Knowledge of system security and data protection practices
 </li>
 <li>
 Experience designing scalable distributed systems
 </li>
 <li>
 Exposure to internal developer platforms or tooling
 </li>
 <li>
 Experience optimizing developer workflows using AI tools
 </li>
 </ul>
 
 
</div>

Software Engineer – LLM & Agentic Systems

ImagineArt

<div><h1 class="jobSectionHeader">Principal AI Engineer (LLM Agents &amp; Orchestration)</h1> 
Role Title: Principal AI Engineer (LLM Agents &amp; Orchestration) Team: Chatly Engineering Focus: Building Autonomous &quot;Super Agents&quot;<h2 class="jobSectionHeader"> 
Who We Are</h2>
Vyro is redefining the future of digital creativity. We build cutting-edge content creation tools powered by Artificial Intelligence and Machine Learning, helping millions of creators, designers, and storytellers bring their imagination to life &mdash; effortlessly.
With a global user base of over 5 million active creators every month, Vyro&rsquo;s 20+ AI-powered apps are transforming how people design, edit, and express themselves across images, videos, and beyond.
From intuitive AI photo editors to next-gen video creation platforms, our products are designed to make creativity accessible, fast, and limitless.
At Vyro, we&rsquo;re a team of innovators, builders, and dreamers &mdash; known as Vyronauts &mdash; driven by passion, purpose, and the belief that technology should inspire creativity, not complicate it.<h3 class="jobSectionHeader">
If you&rsquo;re excited about shaping the next wave of AI-powered creativity, Vyro is the place to be.</h3><h3 class="jobSectionHeader"> 

About the Role</h3>
We are looking for a deep expert in Large Language Models (LLMs) to lead the architectural development of our new &quot;Super Agent&quot; within Chatly. You will move beyond simple chat interfaces to build autonomous agents capable of complex reasoning, tool usage, and seamless integration with external workflows.<h3 class="jobSectionHeader">
Key Responsibilities</h3><ul><li>
Agent Architecture: Design and implement stateful agentic workflows (using frameworks like LangGraph or custom Python/TypeScript solutions) that can plan, execute, and self-correct.</li><li>
Integration Ecosystem: Build the &quot;hands&quot; of the agent. Develop robust integration layers that allow the LLM to interact with our internal APIs, databases, and third-party tools reliably.</li><li>
Latency &amp; Reliability: Optimize inference pipelines for speed (streaming, token optimization) and reliability (handling hallucinations, structured output validation).</li><li>
Memory Systems: Architect advanced RAG (Retrieval-Augmented Generation) systems to give the Super Agent persistent memory and context awareness across sessions.</li><li>
Evaluation &amp; Observability: Establish a rigorous testing framework for non-deterministic model outputs to ensure the agent behaves as expected in production.</li></ul><h3 class="jobSectionHeader">
Technical Requirements</h3><ul><li>
Core Stack: Deep proficiency in Python (or TypeScript) and the modern AI stack (LangChain, LlamaIndex, DSPy).</li><li>
Model Internals: Strong grasp of how to leverage specific model strengths (e.g., GPT-4o for reasoning, Haiku/Flash for speed) and experience with Function Calling/Tool Use.</li><li>
Vector Search: Hands-on experience with vector databases (Pinecone, Milvus, Weaviate) and embedding strategies.</li><li>
System Design: Experience designing event-driven architectures where agents respond to triggers, not just user prompts.</li></ul>
Why Join Us?<ul><li>
Opportunity to work on innovative AI products like Chatly and Imagine, shaping the future of creativity and user interaction.</li><li>
Be part of a passionate, fast-moving team that values innovation and data-driven decisions.</li><li>
Competitive compensation and benefits.</li><li>
A culture where learning, growth, and experimenting with new ideas are deeply encouraged.</li></ul></div>

Agentic AI Engineer

Greenfield Marine Technologies

We are looking for a hands-on GenAI Engineer to design, build, and deploy production-grade AI applications powered by Large Language Models (LLMs). This role is focused on turning business use cases into reliable, scalable AI solutions such as copilots, knowledge assistants, document intelligence tools, and workflow automation systems.The ideal candidate combines strong software engineering skills with practical experience in LLM application development, Retrieval-Augmented Generation (RAG), prompt engineering, model evaluation, and API integration. This person should be comfortable moving from prototype to production while optimizing for quality, latency, security, and cost. This builds on the original JD&rsquo;s emphasis on LLM apps, RAG, integrations, vector databases, and cloud deployment.Key Responsibilities:<ul><li>Design, develop, and deploy GenAI applications using LLMs for real-world business workflows.</li><li>Build AI-powered solutions such as chatbots, copilots, knowledge assistants, search assistants, and workflow automation tools</li><li>Architect and optimize RAG pipelines using structured and unstructured enterprise data.</li><li>Develop robust prompt strategies, guardrails, and response orchestration for accuracy and safety.</li><li>Integrate AI capabilities into web applications, backend systems, APIs, and enterprise platforms.</li><li>Work with vector databases and retrieval systems to improve relevance and response quality.</li><li>Evaluate model performance using defined quality metrics, test cases, and user feedback loops.</li><li>Improve production performance across latency, cost, scalability, observability, and reliability.</li><li>Collaborate with product, engineering, and business teams to translate use cases into deployable AI features.</li><li>Stay updated with emerging GenAI tools, frameworks, and model capabilities, and recommend practical adoption where relevant.</li></ul>Must-Have Skills: Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Strong hands-on programming experience in Python. Experience building GenAI applications using frameworks such as LangChain, LlamaIndex, or equivalent orchestration tools. Experience working with OpenAI APIs, Anthropic, Bedrock, Azure OpenAI, or similar LLM platforms. Strong understanding of prompt engineering, RAG architecture, and LLM application design. Experience integrating AI models with REST APIs, web applications, and backend services. Familiarity with vector databases such as FAISS, Pinecone, Weaviate, Chroma, or similar. Good understanding of machine learning and NLP fundamentals.Experience deploying production-ready applications on AWS, Azure, or GCP.Ability to evaluate and improve model quality, including hallucination reduction and retrieval performance.Understanding of security, access control, and safe handling of enterprise data in AI applications.Preferred Skills : Experience with Amazon Bedrock. Experience with multimodal AI applications involving text, images, PDFs, and document processing. Familiarity with fine-tuning, adapters, or domain adaptation techniques for LLMs.Experience with Docker, Kubernetes, CI/CD pipelines, and MLOps workflows.Exposure to observability and monitoring tools for AI systems.Experience building domain-specific assistants for operations, compliance, technical support, or enterprise knowledge systems.Experience Required:2&ndash;3 years of experience in software engineering, machine learning, or AI application development.At least 1&ndash;2 years of practical experience working on GenAI / LLM-based applications in production or advanced PoCs.Job Types: Full-time, PermanentAbility to commute/relocate:<ul><li>Abu Dhabi: Reliably commute or planning to relocate before starting work (Required)</li></ul>Application Question(s):<ul><li>What is your Visa Status?</li><li>What is your Current Salary?</li><li>What is your Expected Salary?</li><li>Visa Expiry Date?</li><li>Duration of Notice Period?</li><li>Mention your available dates for an online interview</li></ul>Experience:<ul><li>GenAI: 2 years (Required)</li></ul>Location:<ul><li>Abu Dhabi (Required)</li></ul>Work Location: In person

GenAI Engineer

Jorofy Private Limited

Role Summary:We are seeking an exceptional Senior AI Engineer to join our team and drive the development of cutting-edge AI solutions that includes conversational, Agentic AI's. This role focuses on building advanced conversational AI systems, implementation AI Agents across different use cases, implementing sophisticated RAG architectures, and deploying production-grade voice AI applications. The ideal candidate will have deep expertise in LLM technologies and a proven track record of delivering scalable AI solutions.Responsibilities:AI System Development-Design and implement advanced Retrieval-Augmented Generation (RAG) systems with sophisticated chunking strategies, multi-vector retrieval, and hybrid search architectures-Develop and optimize conversational AI agents with complex multi-turn dialogue management and context awareness-Build production-ready voicebot solutions integrating real-time speech processing pipelines-Architect and deploy Text-to-Speech (TTS) and Speech-to-Text (STT) systems with low latency and high accuracy-Design and Deploy PROD grade AI Agents that can scale up for various use cases.LLM Engineering-Fine-tune large language models using various techniques.-Optimize model performance through prompt engineering, few-shot learning, and retrieval strategies-Implement evaluation frameworks and metrics for LLM outputs and conversational quality-Develop custom training pipelines for domain adaptation and specialized use cases-Design scalable AI infrastructure supporting real-time inference and high-throughput batch processing-Implement efficient vector databases and semantic search systems (Pinecone, Azure Search)-Build monitoring and observability solutions for AI systems in productionStakeholder Management &amp; Governance-Coordinate across organizational stakeholders including Architecture, Security, Data Gov etc.-Ensure compliance with internal and external governance frameworks and regulations-Advocate for AI trends and technological advancements relevant to telecommunicationsStay current with latest developments in LLM architectures, RAG techniques, and voice AI technologiesExperiment with emerging models and frameworks to evaluate applicability to business problemsApply at : careers@enums.coJob type : RemoteJob Type: Full-timeApplication Question(s): * It's Not a Part time job, It's a Full time role. You can not do any other job or freelance work if you need this full time job.Education:<ul><li>Bachelor's (Required)</li></ul>Experience:<ul><li>AI Lead: 8 years (Required)</li></ul>Work Location: Remote

Senior AI Engineer

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