generative ai engineer

Dutech Systems

<div>
 Austin,TX
 
 
 DatePosted : 4/22/2026 8:44:04 AM
 
 
 JobNumber : DTS1017187687
 
 
 JobType : W2-Contract to Hire
 
 
 Skills: Generative AI, LLMs, OpenAI API, LangChain, LlamaIndex, Agentic AI, RAG, Vector Databases, Pinecone, FAISS, Weaviate, Python, Machine Learning, Neural Networks, Azure AI, AWS Bedrock, Snowflake Cortex, MLOps, AI Governance
 
 
 
 Job Description
 
 
 
 
 Immediate Hiring | Generative AI | Public Sector | Austin, TX
 
 
 
 We are seeking a skilled Generative AI Engineer to design, develop, and deploy AI-driven automation solutions for public sector use cases such as citizen services, document processing, and compliance workflows. This role focuses on building scalable Generative AI systems, including custom GPTs, agentic workflows, and domain-specific copilots, along with end-to-end ML/AI pipelines.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Design, develop, and deploy AI-driven automation solutions for public sector use cases
 </li>
 <li>
 Build and operationalize Generative AI systems, including custom GPTs and agentic workflows
 </li>
 <li>
 Develop and manage end-to-end ML/AI pipelines (data ingestion, training, evaluation, deployment, monitoring)
 </li>
 <li>
 Collaborate with stakeholders to translate business requirements into AI-enabled solutions
 </li>
 <li>
 Ensure compliance with public sector standards for security, privacy, governance, and ethical AI usage
 </li>
 <li>
 Apply data science techniques including supervised, unsupervised learning, and neural networks
 
 
 </li>
 </ul>
 
 Required Skills
 
 
 
 <ul>
 <li>
 Hands-on experience with Generative AI frameworks (OpenAI APIs, LangChain, LlamaIndex) and custom GPT development
 </li>
 <li>
 Strong foundation in machine learning (supervised/unsupervised learning, model evaluation, lifecycle management)
 </li>
 <li>
 Experience building agentic AI systems (Azure AI Foundry, AWS Bedrock, Snowflake Cortex, autonomous workflows)
 </li>
 <li>
 Knowledge of vector databases (Pinecone, FAISS, Weaviate) and RAG architecture
 </li>
 <li>
 Proficiency in Python and/or R
 </li>
 <li>
 Experience with cloud platforms (Azure, AWS, or GCP) and MLOps tooling
 
 
 </li>
 </ul>
 
 Customer Engagement &amp; Training
 
 
 
 <ul>
 <li>
 Serve as a technical liaison to public sector customers
 </li>
 <li>
 Conduct workshops, training sessions, and enablement programs
 </li>
 <li>
 Provide guidance on AI adoption, governance, and change management
 </li>
 <li>
 Support pilot implementations, proofs of concept (POCs), and scaling initiatives
 </li>
 <li>
 Develop documentation, playbooks, and best practices
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 3&ndash;5 years of experience in AI/ML engineering, data science, or automation roles
 </li>
 <li>
 Experience delivering production-grade AI/ML or GenAI solutions
 </li>
 <li>
 Experience managing AI/ML lifecycle (versioning, monitoring, retraining, optimization)
 </li>
 <li>
 Strong communication and stakeholder engagement skills
 </li>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Engineering, Data Science, or related field
 </li>
 </ul>
</div>

Generative AI Engineer (LLM & Agentic Systems)

Backticksai

<div>
 
 
 Overview
 
 
 
 
 
 
 
 
 
 We are looking for an AI/Data Engineer who can work with complex, messy datasets and turn them into
 
 
 meaningful insights and AI-driven opportunities. This role is focused on understanding data deeply,
 
 
 identifying gaps and patterns, and translating them into practical, high-impact use cases.
 
 
 
 
 
 This is not a purely modeling role &mdash; it requires strong analytical thinking, curiosity, and the ability to
 
 
 connect data with real-world problems. The role also involves client interaction, discussions, and
 
 
 participation in interviews, so strong communication skills are essential.
 
 
 
 
 
 
 
 
 
 What You&rsquo;ll Do
 
 
 <ul>
 <li>
 Explore and analyze structured and unstructured datasets to understand data coverage, quality, and
 </li>
 <li>
 relationships
 </li>
 <li>
 Identify data gaps, inconsistencies, and missing signals that may impact downstream use cases
 </li>
 <li>
 Detect patterns, trends, and anomalies to generate actionable insights
 </li>
 <li>
 Translate raw data and ambiguous problems into clear AI/ML use cases
 </li>
 <li>
 Validate ideas by building lightweight prototypes (e.g., simple models, SQL pipelines, or LLM-based
 </li>
 <li>
 solutions)
 </li>
 <li>
 Collaborate with stakeholders and directly interact with clients to refine problems and prioritize high-
 </li>
 <li>
 impact opportunities
 </li>
 <li>
 Participate in technical discussions, client calls, and interviews when required
 </li>
 <li>
 Continuously question assumptions and improve how data is collected, structured, and utilized
 </li>
 </ul>
 
 
 
 
 
 What We&rsquo;re Looking For
 
 
 <ul>
 <li>
 Bachelor&rsquo;s in Computer Science, Data Science, AI, Engineering, or related field (Master&rsquo;s is a plus)
 </li>
 <li>
 1&ndash;3 years of experience in data analysis, data engineering, data science, or applied AI/ML
 </li>
 <li>
 Strong Python (pandas, numpy) and SQL skills
 </li>
 <li>
 Experience working with structured and unstructured data (logs, emails, tickets, tables)
 </li>
 <li>
 Strong EDA skills with ability to identify patterns, anomalies, and data gaps
 </li>
 <li>
 Solid understanding of ML fundamentals (classification, clustering, evaluation) Experience or familiarity with NLP (text analysis, summarization, sentiment analysis)
 </li>
 <li>
 Familiarity with AI tools (Copilot, Cursor, etc.) and cloud platforms (Azure or AWS)
 </li>
 <li>
 Understanding of data engineering concepts (ETL/ELT, data pipelines, data cleaning)
 </li>
 <li>
 Strong problem-solving and analytical thinking
 </li>
 <li>
 Comfortable with ambiguity and messy data
 </li>
 <li>
 Good communication skills with ability to interact with clients and participate in
 </li>
 <li>
 discussions/interviews
 </li>
 </ul>
 
 
 
 
 
 Nice to Have
 
 
 <ul>
 <li>
 Exposure to LLMs or advanced NLP tasks
 </li>
 <li>
 Experience with data visualization tools (Power BI, Tableau, matplotlib)
 </li>
 </ul>
 
 
 
 
 
 Important Note
 
 
 
 
 
 <ul>
 <li>
 
 Willingness to work onsite at Johar Town (Lahore); this is an onsite role, and candidates not available
 
 </li>
 <li>
 
 for onsite work will not be considered
 
 </li>
 <li>
 
 Working Hours: Alignment with EST timezone (Eastern Standard Time) &mdash; expected availability from
 
 </li>
 <li>
 
 5:00 PM PKT onwards
 
 </li>
 <li>
 
 Candidates with a primary focus on Computer Vision are strictly not considered for this role.
 
 </li>
 </ul>
</div>

Generative AI Engineer

Openkyber

<div>Job Summary: We are seeking a Generative AI Engineer to design, build, and scale agentic and generative AI solutions that deliver measurable business outcomes. This is a hands-on role involving the development of AI-powered applications, contributing to technical strategy, and supporting the delivery of scalable, production-grade systems. The ideal candidate will work across AI/ML and software engineering domains, integrating advanced AI capabilities into enterprise platforms.
Key Responsibilities:
<ul><li>Design, develop, test, and deploy AI-enabled services and applications across environments</li>
<li>Build and scale generative AI solutions using large language models (LLMs), retrieval-augmented generation (RAG), and agentic frameworks</li>
<li>Develop production-grade agentic solutions using frameworks such as LangChain and LangGraph</li>
<li>Build and orchestrate multi-agent systems using tools such as Google ADK and related ecosystems</li>
<li>Integrate AI capabilities into existing enterprise systems and applications</li>
<li>Engineer secure execution patterns, ensuring compliance with enterprise security and integration standards</li>
<li>Evaluate technical challenges related to AI technologies, orchestration approaches, and model behavior</li>
<li>Troubleshoot and resolve complex issues across AI services and models</li>
<li>Implement monitoring, evaluation, and lifecycle management for AI systems, including performance and drift tracking</li>
<li>Contribute to technical strategy, platform standards, and operational readiness for AI initiatives</li>
<li>Collaborate with cross-functional teams to deliver high-quality solutions</li>
<li>Provide technical guidance and mentorship to team members</li>
<li>Ensure alignment with organizational policies, compliance requirements, and risk management standards</li>
</ul>Required Qualifications:
<ul><li>Strong experience in software engineering and AI/ML development</li>
<li>Hands-on experience building generative AI and LLM-based applications in production environments</li>
<li>Experience with frameworks such as LangChain, LangGraph, or similar</li>
<li>Proficiency in designing and implementing RAG-based solutions and agentic architectures</li>
<li>Experience integrating AI systems into enterprise applications and services</li>
<li>Strong understanding of orchestration, multi-agent systems, and AI model behavior</li>
<li>Experience troubleshooting and optimizing AI systems for performance and reliability</li>
<li>Familiarity with monitoring, evaluation, and lifecycle management of AI models</li>
<li>Strong problem-solving and analytical skills</li>
<li>Effective communication and collaboration skills</li>
</ul>Preferred Qualifications:
<ul><li>Experience with Google ADK or similar multi-agent development platforms</li>
<li>Familiarity with MCP-based tool integration and agent-to-agent communication patterns</li>
<li>Experience implementing secure, enterprise-grade AI systems</li>
<li>Experience mentoring or guiding engineering teams</li>
<li>Exposure to large-scale AI platform strategy and architecture</li>
</ul>Education: Bachelors Degree
For applications and inquiries, contact: hirings@openkyber.com</div>

AI/ML Strategy Consultant

MASTER-WORKS

<div>Master Works is seeking an AI Engineer with strong generative AI prototyping experience to rapidly turn ideas into working AI-powered demos, proofs of concept, and early-stage products. The role sits between research and production engineering &mdash; combining hands-on use of large language models, multimodal models, retrieval-augmented generation, and agent frameworks to validate feasibility, explore new capabilities, and de-risk feature delivery. The role is central to accelerating AI innovation across the data, analytics, and intelligence portfolio, with a particular emphasis on bilingual (Arabic/English) AI use cases.<ul><li>
Rapidly design and build generative-AI prototypes and proofs of concept &mdash; from initial idea to working demo &mdash; using modern LLM APIs, open-source models, and prototyping frameworks.</li><li>
Design and implement retrieval-augmented generation (RAG) pipelines, including document ingestion, chunking strategies, embedding generation, vector storage, and retrieval orchestration.</li><li>
Build agentic systems that use tool/function calling, planning, and multi-step reasoning to automate complex tasks; integrate AI agents with internal APIs, databases, and external services.</li><li>
Engineer prompts, system instructions, and structured-output schemas; iterate on prompt design through measurable evaluation rather than guesswork.</li><li>
Work with multimodal AI capabilities &mdash; text, vision, speech (STT/TTS), and document understanding &mdash; and combine them into coherent end-to-end AI pipelines.</li><li>
Build and run AI evaluation frameworks (offline benchmarks, LLM-as-judge, golden-set regression testing) to measure quality, accuracy, latency, and cost across model and prompt variants.</li><li>
Adapt and fine-tune open-source models (LoRA, QLoRA, full fine-tuning) when proprietary or domain-specific behaviour is required, especially for Arabic-language tasks.</li><li>
Apply responsible-AI practices &mdash; safety filtering, hallucination mitigation, prompt-injection defence, PII handling, and bias awareness &mdash; especially for sensitive or regulated environments.</li><li>
Hand off mature prototypes to production engineering teams, providing clear documentation, evaluation results, deployment notes, and migration guidance.</li><li>
Demo prototypes and AI capabilities to internal stakeholders and clients; explain technical concepts, limitations, and risks to non-technical audiences.</li><li>
Track and evaluate fast-moving developments in the generative-AI ecosystem (new models, tools, techniques, and providers) and recommend adoption where they materially improve outcomes.</li><li>
Collaborate cross-functionally with product, design, data, and software engineering teams to shape AI features end-to-end and ensure they deliver real user value.</li></ul> 

Requirements 
<ul><li>
Bachelor&rsquo;s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related discipline.
</li><li>Postgraduate qualification (MSc/PhD) in Machine Learning, Artificial Intelligence, Natural Language Processing, or a related field is preferred.</li><li>
Recognised certifications in machine learning, deep learning, generative AI, or cloud AI platforms (AWS, Azure, GCP) are a strong plus.</li><li>
3&ndash;5+ years of AI/ML engineering experience, with at least 1&ndash;2 years hands-on building generative-AI applications using modern LLMs.</li><li>
Demonstrable portfolio of shipped prototypes or production AI features &mdash; ideally including RAG systems, AI agents, or multimodal pipelines.</li><li>
Hands-on experience with leading LLM APIs (e.g., Anthropic Claude, OpenAI GPT, Google Gemini) and open-source model serving (e.g., Hugging Face, vLLM, Ollama).</li><li>
Strong Python proficiency, including modern AI libraries such as PyTorch, Hugging Face Transformers, LangChain or LlamaIndex, and standard data tooling.</li><li>
Working experience with embedding models, vector databases (e.g., Pinecone, Weaviate, Qdrant, FAISS), and retrieval evaluation techniques.</li><li>
Familiarity with rapid-prototyping tools (Streamlit, Gradio, Jupyter), API integration, and lightweight web back-ends.</li><li>
Experience designing AI evaluation harnesses (offline benchmarks, LLM-as-judge, golden sets) and using observability/tracing tools for AI systems.</li></ul><ul><li>
Exposure to fine-tuning techniques (LoRA, QLoRA, instruction tuning) and to Arabic-language NLP is strongly preferred.</li><li>
Working knowledge of containerisation (Docker), version control (Git), and cloud AI platforms (AWS Bedrock, Azure OpenAI, GCP Vertex AI) is a strong plus.</li><li>
Excellent leadership and team development skills.</li><li>
Strong bias for action and a rapid-prototyping mindset; able to ship a working demo in days, not months.</li><li>
Comfortable working under uncertainty &mdash; rapidly forming hypotheses, running experiments, and iterating based on evidence.</li><li>
Strong analytical and problem-solving skills, with the discipline to evaluate AI behaviour quantitatively rather than relying on intuition alone.</li><li>
Excellent verbal and written communication, with the ability to demo AI capabilities and explain technical concepts, limitations, and risks to non-technical audiences.</li><li>
Curiosity and continuous-learning mindset; actively follows the rapidly evolving generative-AI landscape and brings new ideas back to the team.</li><li>
Collaborative team player; comfortable in cross-functional teams of researchers, software engineers, designers, and client business owners.</li><li>
Cultural awareness and sensitivity, particularly when designing AI experiences for Arabic-speaking users in regional contexts.</li></ul></div>

AI Engineer

Capgemini

<div>
 
 
 Data Scientist
 
 
 
 Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you&rsquo;d like, where you&rsquo;ll be supported and inspired by a collaborative community of colleagues around the world, and where you&rsquo;ll be able to reimagine what&rsquo;s possible. Join us and help the world&rsquo;s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.
 
 
 
 Job Description
 
 
 
 As a Data Scientist , you will lead the development and implementation of advanced data engineering solutions to support the deployment and optimization of Generative AI models. Your role will involve leveraging your extensive experience to design robust, scalable, and innovative data architectures that align with the unique requirements of General Artificial Intelligence (GenAI) applications.
 
 
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 The Machine Learning Engineer will be responsible for architectural design and planning, advanced data pipelines, model integration and optimization, scalability, performance and research and innovation supporting production generative AI systems.
 </li>
 <li>
 Production level ML workloads for customers using Databricks platform, including end-to-end ML pipelines, training/inference optimization, integration with cloud-native services and MLOps
 </li>
 <li>
 Build and maintain data engineering solutions on cloud platforms using hyperscaler services.
 </li>
 <li>
 Develop production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring
 </li>
 <li>
 Design, develop, and maintain data pipelines to efficiently collect, process, and load data from various sources into data storage systems (e.g., data warehouses, data lakes).
 </li>
 <li>
 Understanding indexing and vectorization to use with Generative AI prompt engineering.
 </li>
 <li>
 Strong understanding of fundamental data science concepts in NLP, including selection and understanding of embedding models.
 </li>
 <li>
 Use hyperscaler technologies to support data needs for expansion of Machine Learning/Data Science capabilities including generative AI.
 </li>
 <li>
 Design, develop, and implement scalable data pipelines and ETL/ELT processes using Python, PySpark and API integrations.
 </li>
 </ul>
 
 
 Required Skill and Experience
 
 
 
 
 <ul>
 <li>
 Bachelor's degree in computer science, data engineering, or a related field with 3+ year's experience (Master's preferred).
 </li>
 <li>
 Proven experience in data engineering, MLOps, ETL, and database management, QL and data manipulation languages.
 </li>
 <li>
 Azure, Python, Java, or Scala.
 </li>
 <li>
 data warehousing platforms (e.g., Databricks, Amazon Redshift, Snowflake) and big data technologies (e.g., Hadoop, Spark).
 </li>
 <li>
 highly scalable Data stores, Data Lake, Data Warehouse, Lakehouse, and unstructured datasets
 </li>
 </ul>
</div>

Data Scientist

<div>We are looking for FullStack AI Engineer for our client in Malvern, PA Job Title: FullStack AI Engineer Job Location: Malvern, PA Job Type: Contract Job Description: Pay Range: $65hr - $70hr Solution Design Implementation Design and implement endtoend generative AI solutions leveraging services like Amazon Bedrock including Agents Knowledge Bases and Guardrails. Model Development Optimization Finetune and customize large language models LLMs and other generative models GANs VAEs for specific business needs ensuring optimal performance and scalability. Prompt Engineering RAG Apply prompt engineering methodologies and build RetrievalAugmented Generation RAG systems to enhance model accuracy reliability and contextual understanding. Build CICD pipelines to automate the model lifecycle including training deployment monitoring and retraining. Cloud Infrastructure IaC Develop and manage robust cloud infrastructure using Infrastructure as Code IaC tools like AWS CloudFormation AWS CDK or Terraform. Collaboration Documentation Work with product managers to integrate AI capabilities into existing systems and create technical documentation best practices and tutorials. Security Compliance Ensure all AI solutions meet AWS best practices for security compliance scalability and cost optimization. Proficiency in Python is essential along with experience in languages like Java or TypeScript. Handson experience with core AWS services including S3 Lambda EC2 DynamoDB API Gateway Step Functions Glue and Athena Strong understanding and experienc in orchestration frameworks like LangChain. Technical Concepts Expertise in Generative AI architectures LLMs vector databases statistical methods and data processing. Strong problemsolving analytical and communication skills for working in crossfunctional teams and interacting with customers.
For applications and inquiries, contact: hirings@openkyber.com</div>

OCI AI Infrastructure Engineer

Master Works

<div>
 Master Works is seeking an AI Engineer with strong generative AI prototyping experience to rapidly turn ideas into working AI-powered demos, proofs of concept, and early-stage products. The role sits between research and production engineering &mdash; combining hands-on use of large language models, multimodal models, retrieval-augmented generation, and agent frameworks to validate feasibility, explore new capabilities, and de-risk feature delivery. The role is central to accelerating AI innovation across the data, analytics, and intelligence portfolio, with a particular emphasis on bilingual (Arabic/English) AI use cases.
 
 
 <ul>
 <li>
 Rapidly design and build generative-AI prototypes and proofs of concept &mdash; from initial idea to working demo &mdash; using modern LLM APIs, open-source models, and prototyping frameworks
 </li>
 <li>
 Design and implement retrieval-augmented generation (RAG) pipelines, including document ingestion, chunking strategies, embedding generation, vector storage, and retrieval orchestration
 </li>
 <li>
 Build agentic systems that use tool/function calling, planning, and multi-step reasoning to automate complex tasks; integrate AI agents with internal APIs, databases, and external services
 </li>
 <li>
 Engineer prompts, system instructions, and structured-output schemas; iterate on prompt design through measurable evaluation rather than guesswork
 </li>
 <li>
 Work with multimodal AI capabilities &mdash; text, vision, speech (STT/TTS), and document understanding &mdash; and combine them into coherent end-to-end AI pipelines
 </li>
 <li>
 Build and run AI evaluation frameworks (offline benchmarks, LLM-as-judge, golden-set regression testing) to measure quality, accuracy, latency, and cost across model and prompt variants
 </li>
 <li>
 Adapt and fine-tune open-source models (LoRA, QLoRA, full fine-tuning) when proprietary or domain-specific behaviour is required, especially for Arabic-language tasks
 </li>
 <li>
 Apply responsible-AI practices &mdash; safety filtering, hallucination mitigation, prompt-injection defence, PII handling, and bias awareness &mdash; especially for sensitive or regulated environments
 </li>
 <li>
 Hand off mature prototypes to production engineering teams, providing clear documentation, evaluation results, deployment notes, and migration guidance
 </li>
 <li>
 Demo prototypes and AI capabilities to internal stakeholders and clients; explain technical concepts, limitations, and risks to non-technical audiences
 </li>
 <li>
 Track and evaluate fast-moving developments in the generative-AI ecosystem (new models, tools, techniques, and providers) and recommend adoption where they materially improve outcomes
 </li>
 <li>
 Collaborate cross-functionally with product, design, data, and software engineering teams to shape AI features end-to-end and ensure they deliver real user value
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 <ul>
 <li>
 Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related discipline.
 </li>
 <li>
 Postgraduate qualification (MSc/PhD) in Machine Learning, Artificial Intelligence, Natural Language Processing, or a related field is preferred
 </li>
 <li>
 Recognised certifications in machine learning, deep learning, generative AI, or cloud AI platforms (AWS, Azure, GCP) are a strong plus
 </li>
 <li>
 3-5+ years of AI/ML engineering experience, with at least 1-2 years hands-on building generative-AI applications using modern LLMs
 </li>
 <li>
 Demonstrable portfolio of shipped prototypes or production AI features &mdash; ideally including RAG systems, AI agents, or multimodal pipelines
 </li>
 <li>
 Hands-on experience with leading LLM APIs (e.g., Anthropic Claude, OpenAI GPT, Google Gemini) and open-source model serving (e.g., Hugging Face, vLLM, Ollama)
 </li>
 <li>
 Strong Python proficiency, including modern AI libraries such as PyTorch, Hugging Face Transformers, LangChain or LlamaIndex, and standard data tooling
 </li>
 <li>
 Working experience with embedding models, vector databases (e.g., Pinecone, Weaviate, Qdrant, FAISS), and retrieval evaluation techniques
 </li>
 <li>
 Familiarity with rapid-prototyping tools (Streamlit, Gradio, Jupyter), API integration, and lightweight web back-ends
 </li>
 <li>
 Experience designing AI evaluation harnesses (offline benchmarks, LLM-as-judge, golden sets) and using observability/tracing tools for AI systems
 </li>
 <li>
 Exposure to fine-tuning techniques (LoRA, QLoRA, instruction tuning) and to Arabic-language NLP is strongly preferred
 </li>
 <li>
 Working knowledge of containerisation (Docker), version control (Git), and cloud AI platforms (AWS Bedrock, Azure OpenAI, GCP Vertex AI) is a strong plus
 </li>
 <li>
 Excellent leadership and team development skills
 </li>
 <li>
 Strong bias for action and a rapid-prototyping mindset; able to ship a working demo in days, not months
 </li>
 <li>
 Comfortable working under uncertainty &mdash; rapidly forming hypotheses, running experiments, and iterating based on evidence
 </li>
 <li>
 Strong analytical and problem-solving skills, with the discipline to evaluate AI behaviour quantitatively rather than relying on intuition alone
 </li>
 <li>
 Excellent verbal and written communication, with the ability to demo AI capabilities and explain technical concepts, limitations, and risks to non-technical audiences
 </li>
 <li>
 Curiosity and continuous-learning mindset; actively follows the rapidly evolving generative-AI landscape and brings new ideas back to the team
 </li>
 <li>
 Collaborative team player; comfortable in cross-functional teams of researchers, software engineers, designers, and client business owners
 </li>
 <li>
 Cultural awareness and sensitivity, particularly when designing AI experiences for Arabic-speaking users in regional contexts
 </li>
 </ul>
</div>

AI engineer

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