ai data labeler

PepsiCo

<div>
 
 Overview
 
 
 
 
 We Are PepsiCo
 
 
 
 PepsiCo is a leading name in North Africa&rsquo;s food and beverage industry, where our products are enjoyed by consumers daily across the region. We offer a robust portfolio of beloved brands like PEPSI, SEVEN-UP, DORITOS, CHEETOS and CHIPSY, designed to cater to the tastes and preferences of consumers. With a focus on local sourcing, production, and distribution, PepsiCo provides essential support to local economies and partners closely with communities throughout the region.
 
 
 
 Guiding Vision and Regional Impact
 
 
 
 PepsiCo&rsquo;s vision aligns with our global goal to be the Leader in Beverages and Convenient Foods by Winning with PepsiCo Positive (pep+). This strategic transformation is committed to sustainable growth by putting people and the planet at the heart of our operations. In North Africa, this includes initiatives in responsible sourcing, waste reduction, and water conservation. Our regional programs empower local talent, boost employment, and provide support for local agriculture, particularly through partnerships with farmers and suppliers.
 
 
 
 Empowering Our People and Communities
 
 
 
 Our dynamic teams drive the PepsiCo culture in North Africa, embracing diversity and collaboration to solve local challenges and create innovative products. We are dedicated to fostering an inclusive workplace, one where every individual feels valued and empowered to be themselves. This commitment extends beyond our employees to positively impact the communities we serve, striving for shared growth and a healthier, more sustainable future for all.
 
 
 
 Diversity and Inclusion Commitment
 
 
 
 PepsiCo stands firmly behind our global commitment to diversity, equality, and human rights. We are proud to be an equal opportunity employer, ensuring a fair and welcoming workplace for all, regardless of age, gender, religion, disability, or any other characteristic. Our dedication to inclusion strengthens our workforce and helps drive our business forward, enriching both PepsiCo and the communities in North Africa.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Operations Support &amp; Line Efficiency
 </li>
 <li>
 Ensure smooth operation of production lines (filler, , labeler, conveyors, palletizer,.,etc).
 </li>
 <li>
 Support production during shifts to quickly resolve breakdowns and minimize downtime.
 </li>
 <li>
 Monitor line performance KPIs (NE,TE, downtime, waste).
 </li>
 <li>
 Preventive &amp; Corrective Maintenance
 </li>
 <li>
 Develop and execute preventive maintenance plans for production equipment.
 </li>
 <li>
 Troubleshoot mechanical, electrical, and automation faults on the line.
 </li>
 <li>
 Lead corrective maintenance activities to restore equipment in minimum time.
 </li>
 <li>
 SAP PM &amp; Maintenance Management
 </li>
 <li>
 Create and manage maintenance notifications and work orders using SAP PM.
 </li>
 <li>
 Ensure accurate documentation of breakdowns, interventions, and spare parts usage.
 </li>
 <li>
 Analyze SAP data to track KPIs such as MTTR, MTBF, and PM compliance.
 </li>
 <li>
 Plan maintenance activities and coordinate spare parts availability through SAP.
 </li>
 <li>
 Reliability &amp; Continuous Improvement
 </li>
 <li>
 Perform root cause analysis (RCA) for repetitive failures and implement corrective actions.
 </li>
 <li>
 Drive continuous improvement initiatives to enhance line efficiency and reduce losses.
 </li>
 <li>
 Participate in line optimization, upgrades, and new equipment installation.
 </li>
 <li>
 Safety, Quality &amp; Compliance
 </li>
 <li>
 Ensure compliance with PepsiCo safety standards (GEHMES) and food safety (AIB).
 </li>
 <li>
 Apply safe maintenance practices (LOTO, permit to work).
 </li>
 <li>
 Maintain proper documentation for audits and inspections.
 </li>
 <li>
 Team Coordination &amp; Leadership
 </li>
 <li>
 Supervise technicians and ensure proper execution of maintenance tasks.
 </li>
 <li>
 Coordinate with Production, Quality, and Engineering teams.
 </li>
 <li>
 Provide on-the-job coaching and technical guidance to the maintenance team.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in Mechanical, or Mechatronics Engineering.
 </li>
 <li>
 1&ndash;3 years of experience in maintenance of production lines (FMCG preferred).
 </li>
 <li>
 Strong knowledge of:
 </li>
 <ul>
 <li>
 Filling &amp; packaging machines (KHS, Krones, Sidel, etc.)
 </li>
 <li>
 Conveying systems &amp; automation
 </li>
 <li>
 PLC troubleshooting (Siemens) is a plus
 </li>
 </ul>
 <li>
 Experience in SAP PM is preferred.
 </li>
 </ul>
</div>

Mechanical Maintenance Engineer

The Carlyle Group

<div>
 
 
 Company Profile
 
 
 
 The Carlyle Group (NASDAQ: CG) is a global investment firm with $475 billion of assets under management, across 678 investment vehicles as of March 31, 2026. Founded in 1987 in Washington, DC, Carlyle has grown into one of the world's largest and most successful investment firms, with more than 2,500 professionals operating in 28 offices in North America, Europe, the Middle East, Asia and Australia.
 
 
 
 
 
 Carlyle's purpose is to connect people, ideas, and capital to fuel growth for companies and performance for investors, which range from public and private pension funds to wealthy individuals and families to sovereign wealth funds, unions and corporations. Carlyle invests across three segments - Global Private Equity, Global Credit and Carlyle AlpInvest - and has deep expertise across industries, markets, and geographies.
 
 
 
 
 
 At Carlyle, we believe that a wide spectrum of experiences and viewpoints drives performance and success. Our CEO, Harvey Schwartz, has stated that, "To build better businesses and create value for all of our stakeholders, we are focused on assembling leadership teams with the strongest insights from a range of perspectives." Reflecting this view, emphasis is placed on development, retention and inclusion through our internal processes and seven Employee Resource Groups (ERGs). We cultivate a culture where ideas are openly shared and challenged, connecting diverse expertise and perspectives to drive enduring value.
 
 
 
 
 
 
 Fund or Department Description
 
 
 
 The Data &amp; AI Engineer sits within Carlyle's Enterprise Technology &amp; Data organization and supports firm-wide data and AI initiatives spanning investment platforms, portfolio operations, investor relations, and corporate functions. The role operates within a federated data operating model, partnering with domain engineering teams to implement shared platforms and reusable patterns for data and AI under the technical direction of the Senior AI &amp; Data Architect.
 
 
 
 
 
 
 Position Summary
 
 
 
 The Data &amp; AI Engineer is an experienced, hands-on engineer who turns Carlyle's data and AI architecture into working production systems. Reporting to the Senior AI &amp; Data Architect, this role is responsible for building and operating the pipelines, semantic layers, retrieval systems, and AI-ready data products that power analytics, automation, LLMs, agents, and generative AI applications across the firm.
 
 
 
 
 
 The role requires deep, hands-on expertise across modern data engineering and applied AI engineering. The Data &amp; AI Engineer will implement retrieval-augmented generation (RAG) patterns, embedding and indexing pipelines, vector stores, and semantic models alongside core ELT, streaming, and analytical pipelines - treating LLMs, agents, and copilots as first-class consumers of the data platform.
 
 
 
 
 
 This is a senior individual-contributor engineering role that executes against architectural standards, contributes to their evolution through hands-on learning, and partners closely with data science, AI engineering, governance, and domain teams to deliver trusted, AI-consumable data at enterprise scale.
 
 
 
 
 
 
 What Success Looks Like:
 
 In the first 12 months, this role will deliver foundational AI-ready data pipelines and retrieval components defined in the target-state architecture, productionize one or more priority RAG or agent-grounding use cases, and establish reusable engineering patterns that other domain teams can adopt across the federated data platform.
 
 
 
 
 
 
 In-office requirement:
 
 4 days per week
 
 
 
 
 
 
 Primary Responsibilities:
 
 
 
 
 AI Data Pipelines &amp; Retrieval Systems (&asymp;35%)
 
 
 <ul>
 <li>
 Build and operate AI-ready data pipelines - embedding generation, chunking, indexing, and refresh workflows - that make Carlyle's enterprise data reliably retrievable by LLMs, agents, and generative AI applications.
 </li>
 <li>
 Implement retrieval-augmented generation (RAG) components, including vector store integrations, hybrid search, re-ranking, and grounding logic, against architectural patterns defined by the Senior AI &amp; Data Architect.
 </li>
 <li>
 Develop and maintain tool and function interfaces that allow agents and copilots to query and act on enterprise data safely, with appropriate guardrails, logging, and evaluation hooks.
 </li>
 <li>
 Partner with Data Science and AI Engineering teams to operationalize feature stores, evaluation datasets, and reusable AI data products.
 </li>
 <li>
 Contribute to semantic and context engineering work that powers natural-language analytics, conversational reporting, and AI-driven insights for business users.
 </li>
 </ul>
 
 
 
 
 
 Modern Data Pipeline Engineering (&asymp;30%)
 
 
 <ul>
 <li>
 Design, build, and maintain production-grade ELT, streaming, and transformation pipelines using tools such as dbt, Fivetran and Snowflake.
 </li>
 <li>
 Implement ingestion, modeling, and consumption patterns that meet enterprise standards for scalability, performance, security, resiliency, and cost efficiency.
 </li>
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 Write clean, well-tested Python and SQL; apply software engineering best practices including version control, code review, CI/CD, modular design, and automated testing.
 </li>
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 Productionize new sources and domains under the federated operating model, partnering with domain data engineers to apply shared platform capabilities consistently.
 </li>
 </ul>
 
 
 
 
 
 Semantic Layer &amp; Data Product Development (&asymp;20%)
 
 
 <ul>
 <li>
 Implement semantic models, data contracts, and analytical/dimensional models that enable trusted self-service analytics and reliable AI grounding.
 </li>
 <li>
 Build and maintain reusable data products with clear ownership, documented contracts, and contextual metadata suitable for both human and AI consumers.
 </li>
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 Collaborate with the Senior AI &amp; Data Architect to refine and extend enterprise semantic standards based on what works in production.
 </li>
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 Support discovery and consumption tooling so that analysts, applications, and agents can find and use data products with minimal friction.
 </li>
 </ul>
 
 
 
 
 
 Data Quality, Observability &amp; AI Trust (&asymp;10%)
 
 
 <ul>
 <li>
 Implement data quality checks, lineage capture, and pipeline observability across both data and AI workloads.
 </li>
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 Build logging, evaluation, and monitoring components for AI systems - including prompt and response capture, retrieval metrics, and model performance signals - in line with governance standards.
 </li>
 <li>
 Partner with Data Governance to operationalize metadata, stewardship, and access controls, ensuring AI systems consume enterprise data with the same rigor as human users.
 </li>
 <li>
 Surface issues early, propose remediations, and feed lessons learned back into architectural patterns.
 </li>
 </ul>
 
 
 
 
 
 Collaboration &amp; Engineering Craft (&asymp;5%)
 
 
 <ul>
 <li>
 Participate in architectural design reviews and contribute hands-on engineering perspective to evolving patterns and standards.
 </li>
 <li>
 Mentor junior data engineers and analysts on modern data and AI engineering practices.
 </li>
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 Document patterns, write runbooks, and share knowledge across the federated organization to accelerate adoption of reusable platform capabilities.
 </li>
 </ul>
 
 
 
 
 
 Requirements:
 
 
 
 
 Education &amp; Certifications
 
 
 <ul>
 <li>
 Bachelor's degree, required
 </li>
 <li>
 Concentration in computer science, data engineering, information systems, or a related field, preferred
 </li>
 <li>
 Masters degree, preferred
 </li>
 <li>
 Relevant certifications in cloud, data engineering, analytics, or AI/ML are preferred
 </li>
 </ul>
 
 
 
 
 
 Professional Experience
 
 
 <ul>
 <li>
 6+ years of overall relevant technical experience, required
 </li>
 <li>
 Experience in data engineering, analytics engineering, or platform engineering, with at least 1-2 years of direct, hands-on experience building generative AI or AI/ML systems in production.
 </li>
 <li>
 Proven experience implementing retrieval, grounding, and semantic components for LLM- or agent-based applications, including RAG pipelines, vector stores, embedding workflows, and structured tool use.
 </li>
 <li>
 Hands-on experience with one or more modern AI platforms and tooling categories (e.g., AWS Bedrock, Databricks ML, Snowflake Cortex, OpenAI/Anthropic APIs, LangChain/LlamaIndex or equivalents, MLflow, and vector databases such as Databricks Vector Search, pgvector, or Pinecone).
 </li>
 <li>
 Strong, demonstrable expertise in Python and SQL, with working knowledge of distributed processing frameworks (e.g., Spark).
 </li>
 <li>
 Deep, hands-on experience with modern data stacks - dbt, Fivetran, Snowflake - in AWS-based environments.
 </li>
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 Track record of building data pipelines and products whose consumers include AI systems, not only BI tools and human analysts.
 </li>
 <li>
 Palantir experience a plus.
 </li>
 <li>
 Experience operating within federated data operating models and complex, regulated enterprise environments; financial services experience preferred.
 </li>
 </ul>
 
 
 
 
 
 Competencies &amp; Attributes
 
 
 <ul>
 <li>
 Demonstrated AI-forward instinct: defaults to asking how AI changes what gets built, rather than whether AI can be added later.
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 Fluency in current AI engineering patterns (RAG, agents, tool use, evaluations, guardrails, observability) and the practical trade-offs involved in shipping them.
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 Strong engineering craft: clean code, automated testing, thoughtful design, and a bias toward production-quality systems over prototypes.
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 Pragmatic, delivery-oriented mindset with strong attention to data quality, AI trust, and long-term maintainability; able to distinguish durable engineering decisions from AI hype.
 </li>
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 Collaborative partner to architects, data scientists, AI engineers, and domain teams; comfortable operating in a matrixed, federated organization and in high-visibility transformational initiatives.
 </li>
 </ul>
 
 
 
 
 
 Benefits/Compensation
 
 
 
 The compensation range for this role is specific to the applicable office location and takes into account a wide range of factors, including required and preferred skill sets; prior experience and training; and licenses and/or certifications.
 
 
 
 
 
 The anticipated base salary range for this role is $160,000 to $180,000.
 
 
 
 
 
 In addition to base salary, the hired professional will receive a comprehensive benefits package including retirement benefits, health insurance, life and disability insurance, paid time off, paid holidays, family planning benefits, and wellness programs. The hired professional may also be eligible for an annual discretionary incentive program based on individual and organizational performance.
 
</div>

Data & AI Engineer

Campbell's

<div>Since 1869, we've connected people through food they love. We&rsquo;re proud to be stewards of amazing brands that people trust. Our portfolio includes the iconic Campbell&rsquo;s brand, as well as Cape Cod, Chunky, Goldfish, Kettle Brand, Lance, Late July, Pacific Foods, Pepperidge Farm, Prego, Pace, Rao&rsquo;s Homemade, Snack Factory, Snyder&rsquo;s of Hanover. Swanson, and V8.
Here, you will make a difference every day. You will be supported to build a rewarding career with opportunities to grow, innovate and inspire. Make history with us.
Operational Support Data Engineer &ndash; Agentic AI &amp; ML Ops (Co-op)
<ul><li>We are seeking a motivated and curious Data Engineer &ndash; Agentic AI &amp; ML Ops to join our Enterprise Data &amp; Analytics team. This co-op provides hands-on experience supporting cloud-based data platforms, AI/ML operations, Generative AI, and Agentic AI solutions.
</li></ul><ul><li>You will work with Databricks, Snowflake, Azure, ADLS, ADF, Power BI, Python, PySpark, SQL, LLMs, and modern AI/ML frameworks in an Agile environment.
</li></ul>If you are passionate about data engineering, AI, and automation, we want to hear from you. 
<ul><li>
</li></ul>Key Responsibilities
<ul><li>Build Data &amp; AI Pipelines: Develop and support ETL/ELT pipelines and AI/ML workflows.
</li></ul><ul><li>Data Integration &amp; Transformation: Ingest, transform, and orchestrate data using Python, PySpark, and SQL.
</li></ul><ul><li>Develop Agentic AI Solutions: Build and test AI agents and intelligent workflows for automation and data access.
</li></ul><ul><li>LLM &amp; Prompt Engineering: Design and optimize prompts and workflows using LLMs and GenAI frameworks.
</li></ul><ul><li>AI/ML Development &amp; Automation: Build Python-based scripts, APIs, and notebooks on cloud platforms.
</li></ul><ul><li>Support Analytics &amp; AI/ML: Prepare datasets for reporting, ML models, forecasting, and advanced analytics.
</li></ul><ul><li>Monitor &amp; Support Operations: Troubleshoot pipeline failures, performance issues, and data quality gaps.
</li></ul><ul><li>MLOps &amp; CI/CD: Support deployment, testing, and automation for data and AI solutions.
</li></ul><ul><li>Data Modeling &amp; Semantic Layers: Assist with STTM, data modeling, and reporting datasets.
</li></ul><ul><li>Agile Collaboration: Participate in sprint planning, stand-ups, and retrospectives.
</li></ul>Documentation &amp; Automation: Create runbooks, workflows, and technical documentation. 
<ul><li>
</li></ul>Learning &amp; Development Opportunities
<ul><li>Hands-on experience with Databricks, Snowflake, Azure, ADLS, ADF, and Power BI.
</li></ul><ul><li>Exposure to MLOps, GenAI, Agentic AI, LLMs, CI/CD, and automation.
</li></ul><ul><li>Experience working with AI agents, prompt engineering, and workflow orchestration.
</li></ul><ul><li>Mentorship from Data, AI/ML, and Platform Engineers.
</li></ul>Experience in Agile/Scrum and DevOps/MLOps environments. 
<ul><li>
</li></ul>Qualifications
<ul><li>Pursuing a degree in Computer Science, Data Engineering, Data Science, AI/ML, or related field.
</li></ul><ul><li>Knowledge of SQL, Python/PySpark, ETL/ELT, and APIs.
</li></ul><ul><li>Familiarity with Databricks, Snowflake, Azure, ADLS, ADF, Power BI, or Git is a plus.
</li></ul><ul><li>Exposure to LLMs, GenAI, Agentic AI, MLOps, or CI/CD is beneficial.
</li></ul><ul><li>Experience with Python-based AI/ML projects or notebooks is a plus.
</li></ul><ul><li>Strong analytical and problem-solving skills.
</li></ul><ul><li>Strong communication and teamwork skills.
</li></ul>The Company is committed to providing equal opportunity for employees and qualified applicants in all aspects of the employment relationship, including consideration for employment, without regard to race, color, sex, sexual orientation, gender identity, national origin, citizenship, marital status, protected veteran status, disability, age, religion, or any other classification protected by law.</div>

Data Engineer – Agentic AI & ML Ops (Co-op)

<div>
 
 Introduction
 
 
 
 A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You&rsquo;ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you&rsquo;ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You&rsquo;ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.
 
 
 
 Your Role And Responsibilities
 
 
 
 As a Data Engineer specializing in Data Platforms-Databricks, you will play a critical role in building and enhancing the Databricks platform for client's Data and AI use cases. This role involves implementing Data and AI use cases on the Databricks platform, requiring strong technical expertise and collaboration. Your primary responsibilities will include:
 
 
 <ul>
 <li>
 Implement Data and AI use cases: Implement data and AI use cases on the Databricks platform, ensuring seamless integration and optimal performance.
 </li>
 <li>
 Enhance Databricks Platform: Build and enhance the Databricks platform to meet client needs, leveraging expertise in data engineering and cloud technologies.
 </li>
 <li>
 Support Client Use Cases: Collaborate with clients to understand their data and AI requirements, providing technical expertise to implement use cases on the Databricks platform.
 
 
 </li>
 </ul>
 
 Required Technical And Professional Expertise
 
 
 
 <ul>
 <li>
 Databricks Platform Expertise: Experience with building and enhancing Databricks platforms for data and AI use cases, including implementing data and AI solutions.
 </li>
 <li>
 Data Engineering Skills: Experience with data engineering principles and practices, including data processing, storage, and retrieval on cloud-based platforms.
 </li>
 <li>
 Cloud Technology Knowledge: Experience with cloud technologies, including migration, deployment, and management of data and AI applications on cloud-based infrastructure.
 </li>
 <li>
 Implementation Expertise: Experience with implementing data and AI use cases on Databricks platforms, ensuring seamless integration and optimal performance.
 </li>
 <li>
 Technical Collaboration: Experience working collaboratively with clients to understand their data and AI requirements, providing technical expertise to implement use cases on the Databricks platform.
 
 
 </li>
 </ul>
 
 Preferred Technical And Professional Experience
 
 
 
 <ul>
 <li>
 Cloud Migration Knowledge: Exposure to migrating data and AI applications to cloud-based infrastructure, including deployment and management on Databricks platforms.
 </li>
 <li>
 Advanced Data Engineering: Exposure to advanced data engineering principles and practices, including data processing, storage, and retrieval on cloud-based platforms.
 </li>
 <li>
 AI Solution Implementation: Exposure to implementing AI solutions on Databricks platforms, ensuring seamless integration and optimal performance.
 </li>
 </ul>
</div>

Senior Data Engineer - Data Integration - Databricks

M&N Business

Job Title: AI &amp; Machine Learning Instructor (Visiting Faculty)Company: PNY Trainings &ndash; PNY GeniusLocation: Rawalpindi (RWP Branch)Job Type: Visiting / Part-Time InstructorSchedule:<ul><li>Days: Wednesday to Friday (Week Dayz)</li><li>Time: 02:00 PM &ndash; 04:00 PM</li><li>Mode: Online + On-Campus</li><li>Batch Duration: 1 Month (12 Classes)</li><li>Session Start: June 2026</li></ul>About the RolePNY Genius is looking for a passionate and experienced AI &amp; Machine Learning Instructor to deliver our &quot;AI for Young Genius&quot; program. The ideal candidate should be capable of teaching Artificial Intelligence, Machine Learning, and Data AI concepts in an engaging and practical manner to young learners.Key Responsibilities<ul><li>Deliver lectures on AI, Machine Learning, and Data AI.</li><li>Prepare and conduct practical demonstrations and hands-on activities.</li><li>Guide students through AI-based projects and assignments.</li><li>Monitor student performance and provide feedback.</li><li>Maintain attendance and academic records.</li><li>Coordinate with the Academic Department regarding course delivery.</li></ul>Course OutlineModule 1: Introduction to Artificial Intelligence<ul><li>What is AI?</li><li>AI in Everyday Life</li><li>Applications of AI</li></ul>Module 2: Machine Learning Fundamentals<ul><li>Introduction to Machine Learning</li><li>Types of Machine Learning</li><li>Real-World ML Examples</li></ul>Module 3: Data &amp; AI<ul><li>Introduction to Data</li><li>Data Collection and Processing</li><li>Role of Data in AI Systems</li></ul>Module 4: AI Tools &amp; Applications<ul><li>Generative AI Tools</li><li>AI Assistants and Chatbots</li><li>AI for Education and Creativity</li></ul>Module 5: Practical AI Projects<ul><li>AI-Based Mini Projects</li><li>Smart Predictions</li><li>Image and Text Recognition Basics</li></ul>Module 6: Future of AI<ul><li>Emerging AI Trends</li><li>Career Opportunities in AI &amp; Machine Learning</li><li>Ethical Use of Artificial Intelligence</li></ul>Requirements<ul><li>Bachelor's degree in Computer Science, Software Engineering, AI, Data Science, or a related field.</li><li>Practical knowledge of Artificial Intelligence and Machine Learning.</li><li>Teaching or training experience preferred.</li><li>Strong communication and presentation skills.</li><li>Ability to engage and mentor young learners.</li></ul>How to ApplyInterested candidates are encouraged to apply through Indeed or send their updated CV to the Hiring Department.Fatima Shakeel HR Executive PNY Group of Companies 0308-5552218 fatima.shakeel@pnygroup.co www.pnycareer.comPay: Rs1,500.00 - Rs1,800.00 per dayWork Location: In person

AI & Machine Learning Instructor (Visiting Faculty)

InnovationTeam

<div>InnovationTeam is a forward-thinking technology company specialized in delivering advanced AI-driven solutions for enterprises and government organizations. We are currently seeking a highly skilled AI Team Lead to join our team onsite in Saudi Arabia.
As an AI Team Lead at InnovationTeam, you will be responsible for leading the design, development, implementation, and delivery of end-to-end AI solutions across multiple domains, including Generative AI, machine learning, deep learning, data science, computer vision, NLP, intelligent automation, and enterprise AI systems.
This role is not limited to Generative AI. It requires a strong technical background across modern AI technologies, hands-on experience in building production-ready AI solutions, and the leadership capability to manage AI engineers, data scientists, ML engineers, and cross-functional delivery teams.
The ideal candidate is a strong technical leader who can translate business requirements into scalable AI solutions, manage project execution, guide technical teams, communicate with stakeholders, and ensure successful delivery of AI initiatives within scope, timeline, and quality standards.
At InnovationTeam, we value innovation, ownership, collaboration, and continuous learning. We provide a dynamic work environment where technical excellence, leadership, and business impact are highly recognized. 
Requirements 
We are seeking an experienced AI Team Lead to lead onsite AI delivery in Saudi Arabia. The role involves managing AI projects, supervising technical teams, designing AI architectures, developing enterprise-grade solutions, and ensuring successful deployment of AI systems into production.
The candidate should have strong experience in Generative AI, machine learning, deep learning, data science, AI solution architecture, MLOps/LLMOps, cloud deployment, and project management.
Key Responsibilities<ul><li>
Lead the AI team in designing, developing, and delivering enterprise AI solutions.</li><li>
Manage AI projects from requirement gathering to production deployment.</li><li>
Translate business needs into technical AI solution designs and implementation plans.</li><li>
Design and supervise solutions involving Generative AI, LLMs, RAG systems, AI agents, NLP, machine learning, deep learning, and computer vision.</li><li>
Lead data science initiatives, including data analysis, feature engineering, model development, evaluation, and optimization.</li><li>
Build and oversee end-to-end AI pipelines, including data ingestion, preprocessing, model training, inference, monitoring, and continuous improvement.</li><li>
Guide the implementation of machine learning and deep learning models using frameworks such as PyTorch, TensorFlow, Scikit-learn, and related tools.</li><li>
Supervise the development of AI agents, chatbots, recommendation systems, classification models, predictive analytics, and intelligent automation solutions.</li><li>
Define AI architecture, integration patterns, APIs, microservices, and deployment strategies.</li><li>
Ensure AI solutions are scalable, secure, reliable, cost-efficient, and production-ready.</li><li>
Collaborate with business stakeholders, product teams, data teams, software engineers, and cloud teams.</li><li>
Manage task allocation, technical reviews, project milestones, risks, and delivery timelines.</li><li>
Establish best practices for MLOps, LLMOps, model governance, model monitoring, documentation, and quality assurance.</li><li>
Support presales and client discussions by providing technical input, solution proposals, effort estimation, and feasibility assessments.</li><li>
Ensure AI solutions comply with security, privacy, governance, and responsible AI principles.</li><li>
Mentor and coach AI engineers, ML engineers, data scientists, and junior team members.</li><li>
Stay up to date with the latest developments in AI, machine learning, deep learning, Generative AI, cloud AI services, and enterprise AI platforms.</li></ul>
Required Skills &amp; Qualifications<ul><li>
Minimum 5 years of professional experience in AI, machine learning, data science, or related technology fields.</li><li>
Minimum 2&ndash;3 years of experience leading AI teams or managing AI solution delivery.</li><li>
Master&rsquo;s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Computer Engineering, or a related field.</li><li>
Strong hands-on experience in machine learning, deep learning, NLP, Generative AI, and data science.</li><li>
Solid understanding of LLMs, RAG architectures, AI agents, vector databases, prompt engineering, and model evaluation.</li><li>
Strong experience with Python and AI/ML frameworks such as PyTorch, TensorFlow, Scikit-learn, Hugging Face, LangChain, or LlamaIndex.</li><li>
Experience in building and deploying ML/DL models for real-world enterprise use cases.</li><li>
Strong understanding of supervised learning, unsupervised learning, classification, regression, clustering, recommendation systems, forecasting, and anomaly detection.</li><li>
Experience with data preparation, data quality assessment, feature engineering, model training, validation, testing, and performance monitoring.</li><li>
Strong software engineering knowledge, including REST APIs, microservices, Docker, Kubernetes, CI/CD, and Git.</li><li>
Experience deploying AI workloads on cloud platforms such as OCI, Azure, AWS, or GCP. OCI experience is preferred.</li><li>
Knowledge of MLOps and LLMOps practices, including model versioning, monitoring, retraining, evaluation, and deployment automation.</li><li>
Strong project management skills, including planning, estimation, risk management, stakeholder communication, and delivery tracking.</li><li>
Ability to communicate complex technical concepts clearly to business and technical stakeholders.</li><li>
Excellent problem-solving, analytical, leadership, and decision-making skills.</li><li>
Excellent command of English, both written and spoken.</li><li>
Willingness to work onsite in Saudi Arabia.</li></ul>
Nice to Have<ul><li>
Experience in government, healthcare, finance, telecom, or regulated enterprise environments.</li><li>
Experience with GPU-based model training and inference optimization.</li><li>
Experience with computer vision, OCR, document AI, speech AI, or multimodal AI systems.</li><li>
Experience with Azure OpenAI, Oracle AI Services, AWS Bedrock, Google Vertex AI, or similar enterprise AI platforms.</li><li>
Knowledge of AI governance, responsible AI, data privacy, cybersecurity, and compliance frameworks.</li><li>
Experience in presales, technical proposal preparation, and client-facing solution presentations.</li><li>
Arabic language skills are an advantage.</li></ul> 

Benefits 
<ul><li>
Opportunity to lead strategic AI initiatives in Saudi Arabia.</li><li>
Work on advanced AI solutions across Generative AI, machine learning, deep learning, and data science.</li><li>
Leadership role with direct impact on enterprise and government AI transformation projects.</li><li>
Collaborative and innovation-driven work environment.</li><li>
Competitive compensation package.</li><li>
Access to modern AI infrastructure, cloud platforms, and enterprise data environments.</li><li>
Professional growth, leadership development, and continuous learning opportunities.</li></ul></div>

AI Team Lead

Impronics Technologies

<div><div>Job Summary</div><div>
We are looking for an experienced Business Analyst with strong expertise in banking processes and AI-driven solutions. The ideal candidate will work closely with business stakeholders, technology teams, and data science teams to gather requirements, analyze business processes, and support the implementation of AI/ML-based banking solutions. The role requires strong analytical skills, stakeholder management, and knowledge of digital banking and regulatory frameworks.</div><div></div><div> 
Key Responsibilities</div><div>
Business Analysis &amp; Requirement Gathering</div><ul><li>
Gather, analyze, and document business requirements for AI-driven banking solutions.</li><li>
Translate business needs into functional specifications, BRDs, FRDs, user stories, and process flows.</li><li>
Conduct gap analysis, feasibility studies, and impact assessments for AI use cases.</li><li>
Collaborate with product, technology, and data science teams to align AI capabilities with business objectives.</li><li>
Support UAT planning, test case preparation, and validation of AI-enabled solutions.</li></ul><div>
AI &amp; Data-Driven Solutioning</div><ul><li>
Identify opportunities to implement AI/ML, NLP, predictive analytics, and automation across banking operations.</li><li>
Work with data science teams to define model requirements, training datasets, and performance metrics.</li><li>
Support AI solution development for:<ul><li>
Chatbots &amp; voicebots</li><li>
Fraud detection &amp; risk scoring</li><li>
Credit decisioning models</li><li>
Customer segmentation &amp; personalization</li><li>
Intelligent process automation</li></ul></li><li>
Validate AI model outputs to ensure alignment with business and regulatory expectations.</li></ul><div>
Banking Domain Expertise</div><ul><li>
Analyze and optimize banking processes across retail banking, corporate banking, lending, payments, onboarding, KYC/AML, and digital banking channels.</li><li>
Ensure compliance with banking regulations, risk frameworks, and data privacy standards such as GDPR, PCI-DSS, SWIFT CSP, and PDPL.</li><li>
Work closely with compliance and risk teams to support responsible AI governance.</li></ul><div>
Stakeholder Management</div><ul><li>
Act as a bridge between business teams, technology teams, and AI/data teams.</li><li>
Conduct workshops, requirement gathering sessions, and stakeholder discussions.</li><li>
Prepare business cases, executive presentations, and ROI assessments for AI initiatives.</li></ul><div>
Required Skills &amp; Experience</div><ul><li>
6&ndash;10+ years of experience as a Business Analyst, including at least 2+ years in AI/ML or data-driven projects.</li><li>
Strong understanding of banking operations, digital channels, lending, payments, and regulatory frameworks.</li><li>
Hands-on experience with:<ul><li>
AI/ML concepts and model lifecycle</li><li>
NLP, chatbots, RPA, predictive analytics</li><li>
Data visualization tools such as Power BI or Tableau</li><li>
Agile/Scrum methodologies</li></ul></li><li>
Strong experience in documentation including BRDs, FRDs, BPMN, UML, and user stories.</li><li>
Excellent analytical, problem-solving, and communication skills.</li></ul><div>
Preferred Qualifications</div><ul><li>
Experience with AI platforms such as Azure AI, AWS AI/ML, or Google Vertex AI.</li><li>
Certifications such as CBAP, CCBA, PMI-PBA, Scrum Master, or AI-related certifications.</li><li>
Exposure to cloud-based data platforms and MLOps environments.</li></ul><div>
Soft Skills</div><ul><li>
Strong stakeholder engagement and communication skills.</li><li>
Ability to simplify complex AI concepts for non-technical stakeholders.</li><li>
Detail-oriented, proactive, and adaptable in fast-paced banking environments.</li><li>
Strong multitasking and time management abilities.</li></ul></div>

Business Analyst – AI Solutions

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