ai systems architect

PACELearn, Inc.

Job Location: Remore, PKDepartment: TechnologyType: Regular, Full-TimeWorking hours: 6 pm &ndash; 3 am (US Hours)Vision: Transforms students&rsquo; lives through Innovating workforce development models addressing skill set gapsMission: Transforms students&rsquo; lives by curating university programs and mapping them to certifications aligned to the emerging job market through apprenticeship and upskilling programsAt EdTech Ventures, we are committed to upholding the following core values:P*assion |Respect | Accountability | Innovation | Speed | Execution [ PRAISE* ]Overview:We are looking for a Agentic AI Systems Architect with deep expertise in AI systems and enterprise architecture to lead the design and development of the AI-enhanced QuickStart RAD platform. This role requires a balance of technical vision, hands-on experience, and leadership to build an agile, scalable, and intelligent architecture that supports fast-paced innovation.You&rsquo;ll work across AI/ML, backend, frontend, data pipelines, DevOps, and cloud to create a seamless platform that empowers rapid delivery of AI-enabled business applications.Responsibilities, include but are not limited to:Architectural Leadership<ul><li>Lead the technical architecture and development of AI-native applications, leveraging data engineering and data science expertise to drive innovation and scalability. This role will oversee the design, development, and deployment of agentic AI systems, ensuring seamless integration with existing infrastructure and driving business growth.</li></ul>*<ul><li>Architect AI/ML pipelines, APIs, data flow, model serving/inference systems, and integration with low-code/no-code layers.</li><li>Establish architectural standards for AI model integration, data versioning, performance optimization, and security.</li></ul>AI &amp; Platform Integration<ul><li>Drive technical innovation, ensuring system scalability and performance, and delivering business value through AI-powered applications.</li><li>Select and integrate AI/ML platforms (e.g., TensorFlow, PyTorch, Hugging Face, OpenAI, LangChain, etc.) into the RAD environment.</li><li>Design intelligent components such as chatbots, recommender systems, NLP engines, or generative AI assistants for internal use.</li><li>Ensure AI models are easily deployable, monitorable, and reusable via microservices or API-driven architecture.</li></ul>Technology Strategy<ul><li>Guide platform evolution by evaluating emerging AI tools (e.g., LLMs, AutoML, AI agents, vector databases).</li><li>Provide direction on cloud-native deployment (AWS/GCP/Azure), edge AI, and scalable model hosting.</li><li>Oversee selection and adoption of RAD/low-code technologies (e.g., Retool, Mendix, OutSystems, custom internal frameworks).</li></ul>Team &amp; Execution Oversight<ul><li>Lead and mentor engineering, data science, and DevOps teams across architecture, coding, and delivery.</li><li>Conduct design reviews, code reviews, and enforce best practices in security, performance, and scalability.</li><li>Collaborate closely with product and business teams to translate AI capabilities into usable business features.</li></ul>Minimum Qualifications:Education &amp; Experience<ul><li>Bachelor&rsquo;s or Master&rsquo;s in Computer Science, AI, Software Engineering, or related field.</li><li>10+ years of experience in software engineering, with at least 4&ndash;5 years in solution or technical architecture roles.</li></ul>Technical Skills<ul><li>Strong experience in architecture and deploying AI/ML models in production environments.</li><li>Hands-on knowledge of ML Ops, model versioning, vector search (e.g., Pinecone, Weaviate), and APIs (REST/GraphQL).</li><li>Solid experience with cloud services for AI (AWS SageMaker, Azure ML, GCP Vertex AI, etc.).</li><li>Proven experience with modern architecture patterns: microservices, containerization (Docker/Kubernetes), serverless, event-driven systems.</li></ul>AI-Specific Tools &amp; Frameworks<ul><li>LangChain, OpenAI API, RAG pipelines.</li><li>PyTorch, TensorFlow, scikit-learn.</li><li>Vector DBs: FAISS, Milvus, Weaviate, Pinecone.</li><li>ML pipeline orchestration tools: Kubeflow, MLflow, Airflow.</li></ul>Soft Skills &amp; Leadership<ul><li>Strategic thinking with ability to balance speed and scalability in AI product delivery.</li><li>Strong collaboration, leadership, and communication skills.</li><li>Experience working with distributed and cross-functional teams.</li><li>Ability to evaluate trade-offs between technical and business decisions.</li></ul>Competency Identifiers:Technical Expertise in AI and Data Engineering<ul><li>Strong background in designing and implementing AI systems,data pipelines, and architectures</li><li>Proficiency in languages like Python, Java, and relevant frameworks.</li></ul>System Design and Architecture<ul><li>Ability to design scalable, secure, and efficient systems that integrate AI and data engineering components</li><li>Prefecient on microservices architecture and cloud computing.</li><li>Proficient with Cloud and Big data tools such as - S3, BigQuery, Snowflake, Redshift, or Databricks</li></ul>Data Science and Machine Learning<ul><li>Strong understanding of data science principles</li><li>machine learning algorithms</li><li>statistical modeling, with experience in applying these concepts to real-world problems.</li></ul>Leadership &amp; Collaboration<ul><li>Proven ability to lead cross-functional teams, collaborate with stakeholders, and communicate technical concepts to non-technical audiences.</li><li>Willingness to stay up-to-date with emerging technologies and trends in AI, data engineering, and cloud computing, and apply this knowledge to drive innovation and improvement.</li></ul>Delivery &amp; Execution Excellence<ul><li>Owns end-to-end technical delivery and system design</li><li>Enforces architecture governance and documentation standards</li><li>Implements scalable CI/CD, DevSecOps, and observability practices</li><li>Proactive in managing technical debt and scalability challenges</li></ul>Key Performance Indicators (KPIs):1. System Scalability and Performance: Measure the system's ability to handle increased traffic, data volume, and user adoption, with metrics such as latency, throughput, and error rates.2. AI Model Accuracy and Reliability*: Track the accuracy, precision, and recall of AI models, as well as their reliability and consistency in production environments.3. Data Quality and Integrity*: Monitor data quality, integrity, and consistency, with metrics such as data completeness, accuracy, and freshness.4. Time-to-Market and Deployment Frequency*: Measure the time it takes to develop, test, and deploy new AI features and models, with a focus on reducing cycle time and increasing deployment frequency.5. Business Value and ROI*: Track the business value generated by AI-powered applications and features, with metrics such as revenue growth, cost savings, and customer satisfaction.Job Type: Full-timePay: Rs500,000.00 - Rs700,000.00 per monthWork Location: Remote

Agentic AI Systems Architect

Shag Infotech Pvt. Ltd.

Job Summary: Shag Infotech is looking for an experienced AI Architect to design and implement advanced AI systems that enhance our IT and marketing solutions. The ideal candidate will combine deep technical expertise with strategic thinking to build scalable, AI-driven platforms and solutions.Key Responsibilities:<ul><li>Design, develop, and implement AI architectures and frameworks for business and marketing applications.</li><li>Collaborate with data scientists, engineers, and product teams to integrate AI solutions into production.</li><li>Lead AI strategy, ensuring scalable and robust AI systems aligned with business goals.</li><li>Evaluate and implement emerging AI technologies and tools.</li><li>Ensure AI models are reliable, secure, and optimized for performance.</li><li>Provide technical guidance and mentorship to AI and ML teams.</li></ul>Requirements:<ul><li>Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, AI, Data Science, or related field.</li><li>5+ years of experience in AI/ML architecture, data engineering, or AI solution design.</li><li>Strong expertise in AI/ML frameworks (TensorFlow, PyTorch, Keras) and cloud platforms (AWS, Azure, GCP).</li><li>Experience designing scalable AI systems and deploying production-level AI models.</li><li>Solid understanding of NLP, deep learning, computer vision, or recommendation systems.</li><li>Excellent problem-solving, communication, and leadership skills.</li></ul>What We Offer:<ul><li>Opportunity to lead cutting-edge AI projects in IT and marketing.</li><li>Collaborative, innovation-driven work environment.</li><li>Competitive salary with performance-based incentives.</li></ul>Job Type: Full-timeWork Location: In person

AI Architect

Convo

<div>
 The Sr. AI Engineer will design, architect, and implement
 
 advanced AI systems
 
 leveraging
 
 multi-agent frameworks
 
 ,
 
 RAG pipelines
 
 , and
 
 LLM orchestration layers
 
 . You&rsquo;ll be responsible for building intelligent, scalable workflows that enable reasoning, contextual understanding, and autonomous task coordination across various enterprise use cases.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Architect and deploy multi-agent AI systems using frameworks like LangGraph, CrewAI, or custom orchestrators.
 </li>
 <li>
 Design and optimize Retrieval-Augmented Generation (RAG) pipelines for contextual reasoning and dynamic knowledge synthesis.
 </li>
 <li>
 Integrate vector databases and semantic search layers (e.g., Pinecone, Weaviate, FAISS) for efficient knowledge retrieval.
 </li>
 <li>
 Develop modular LLM pipelines combining tools, memory, and dynamic context switching.
 </li>
 <li>
 Implement Python-based AI microservices with robust APIs for reasoning, planning, and data processing.
 </li>
 <li>
 Conduct prompt engineering, evaluation, and adaptive tuning to improve model accuracy and reliability.
 </li>
 <li>
 Build autonomous data ingestion and learning loops to continuously improve AI system performance.
 </li>
 <li>
 Collaborate with cross-functional teams to align AI pipelines with product goals and business logic.
 </li>
 <li>
 Research and prototype new frameworks in agentic AI, self-correcting systems, and multi-modal reasoning.
 
 
 </li>
 </ul>
 
 Required Skills
 
 
 
 <ul>
 <li>
 Minimum 5+ years of experience
 </li>
 <li>
 Strong expertise in LLMs, NLP, RAG architectures, and multi-agent orchestration.
 </li>
 <li>
 Proficiency in Python, LangChain, LangGraph, and OpenAI / Hugging Face APIs.
 </li>
 <li>
 Experience deploying AI pipelines in production environments (microservices or distributed systems).
 </li>
 <li>
 Hands-on with vector DBs, semantic embeddings, and knowledge graphs.
 </li>
 <li>
 Solid grasp of LLM evaluation, memory management, and context-aware reasoning.
 </li>
 <li>
 Experience integrating AI tools with backend systems (e.g., GitHub, Azure DevOps, RESTful APIs and FASTAPIs).
 </li>
 <li>
 Strong understanding of system design, data pipelines, and cloud-based AI architectures.
 
 
 </li>
 </ul>
 
 Nice to Have
 
 
 
 <ul>
 <li>
 Familiarity with LangGraph, CrewAI, LlamaIndex, or Semantic Kernel.
 </li>
 <li>
 Experience building autonomous agents or self-improving task networks.
 </li>
 <li>
 Exposure to Azure OpenAI, Vertex AI, or Anthropic Claude API.
 </li>
 <li>
 Background in AI evaluation, reinforcement learning from human feedback (RLHF), or tool-augmented reasoning.
 </li>
 <li>
 Ability to mentor teams in prompt engineering, AI safety, and agentic framework design.
 </li>
 </ul>
</div>

Senior AI Engineer

GSSTech Group

<div>We are seeking an experienced Infrastructure &amp; AI Systems Manager to lead the design, deployment, and optimization of advanced AI-driven infrastructure solutions. The ideal candidate will have deep expertise in cloud architecture, containerization, and AI system integration, with a focus on building scalable, secure, and compliant platforms.
Requirements<h3 class="jobSectionHeader">
Key Responsibilities:</h3><ul>
<li>Architect Agentic AI Solutions: Design modular systems integrating LLMs to support multi-step reasoning, retrieval-augmented generation (RAG), and tool use.
</li><li>AI Model Integration: Evaluate, select, and integrate suitable models (e.g., GPT-5 or custom LLMs) into scalable environments.
</li><li>AI Agent Development: Build and maintain intelligent agents for research, summarization, and autonomous task execution.
</li><li>Infrastructure &amp; Scalability: Manage infrastructure using Docker and Kubernetes to ensure high availability, scalability, and reliability.
</li><li>Responsible AI Practices: Implement AI governance, including guardrails, content filters, and compliance with data protection standards.
</li><li>Cloud &amp; Security Management: Oversee cloud operations (Azure, AWS) ensuring cost efficiency, performance, and compliance.
</li><li>Collaboration &amp; Stakeholder Management: Work closely with cross-functional teams and customers to translate business objectives into technical AI solutions.
</li><li>Continuous Optimization: Analyze model performance, refine prompts, and enhance system accuracy and efficiency.
</li><li>Data Engineering: Design and manage data pipelines and federated data marts; ensure seamless data movement and optimization across sources.
</li><li>Automation &amp; DevOps: Champion infrastructure-as-code, CI/CD pipelines, and the automation of provisioning, deployment, and monitoring.
</li></ul><h3 class="jobSectionHeader">Qualifications &amp; Experience:</h3><ul>
<li>Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Information Technology, or a related field.
</li><li>8+ years of experience in infrastructure management, cloud engineering, or AI systems architecture.
</li><li>Proven experience with Docker, Kubernetes, and cloud platforms (AWS, Azure).
</li><li>Solid understanding of AI/ML deployment, RAG systems, and LLM model operations.
</li><li>Familiarity with infrastructure security, identity management, and data protection compliance (UAE CB | SIA | SAMA).
</li><li>Strong hands-on experience with DevOps, CI/CD, and automation frameworks.
</li><li>Excellent problem-solving and communication skills with the ability to collaborate across teams and manage complex technical projects.</li></ul></div>

Job Title: Infrastructure & AI Systems Manager

OREDATA

<div>
 
 OREDATA is a Digital Transformation &amp; IT Consulting firm with 10+ years of proven expertise and hundreds of successfully implemented projects across the EMEA region. When you join the OREDATA team, you'll be working hand-in-hand with experts focused on tackling digital, operational, analytical &amp; data science challenges with the greatest impact. We foster collaboration with proximity, an agile and autonomous approach and best practices and guiding principles.
 
 
 Read more: https://medium.com/@oredata-engineering
 
 
 Apply and be part of our exciting journey!
 
 
 
 
 Responsibilities:
 
 
 <ul>
 <li>
 Design and Develop Agentic AI Systems: Architect, develop and deploy intelligent agent systems that exhibit autonomous decision-making and action-taking capabilities, leveraging large language models (LLMs) and other advanced AI techniques
 </li>
 <li>
 Data Analysis &amp; Predictive Modeling: Analyze and interpret large, complex datasets using advanced statistical techniques and machine learning algorithms. Develop and implement predictive models, including recommendation engines, to identify patterns, trends, and actionable insights
 </li>
 <li>
 Feature Engineering &amp; Data Orchestration: Lead the collection, cleaning, organization, and transformation of large, complex datasets from multiple sources into high-quality features for training and evaluating AI models
 </li>
 <li>
 System Integration &amp; Scalability: Build and maintain scalable software infrastructure and data pipelines required for AI/ML and agentic AI deployments. Ensure the seamless integration of AI agents with existing systems and workflows
 </li>
 <li>
 Cross-functional Collaboration &amp; Communication: Collaborate effectively with cross-functional teams, including product managers, data scientists, and business stakeholders, to understand business needs, translate them into AI solutions, and present complex findings and data-driven results in a clear and concise manner to diverse audiences
 </li>
 <li>
 Innovation &amp; Research: Drive innovation in agentic AI methodologies, tools, and applications within the organization. Conduct research and experimentation with new generative AI and agentic techniques, staying abreast of the latest advancements in the field
 </li>
 <li>
 Performance Optimization &amp; Monitoring: Optimize agent workflows for reliability, safety and performance. Implement robust monitoring and evaluation frameworks for deployed AI agent systems
 </li>
 </ul>
 
 
 Requirements:
 
 
 <ul>
 <li>
 Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a relevant field
 </li>
 <li>
 Proven experience
 
 5 to 10 years
 
 in software development, with a strong focus on AI/ML engineering
 </li>
 <li>
 Demonstrated experience&nbsp; in building and deploying AI agent systems or similar autonomous systems
 </li>
 <li>
 Solid understanding of machine learning algorithms, statistical modeling and data analysis techniques
 </li>
 <li>
 Proficiency in Python and relevant AI/ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn)
 </li>
 <li>
 Experience with prompt engineering and fine-tuning Large Language Models (LLMs)
 </li>
 <li>
 Strong problem-solving and analytical skills with an ability to translate complex data into actionable business strategies
 </li>
 <li>
 Excellent communication, presentation, and interpersonal skills, with the ability to convey technical concepts to non-technical stakeholders
 </li>
 </ul>
 
 
 Preferred Qualifications
 
 
 <ul>
 <li>
 Experience with cloud computing platforms, particularly Google Cloud Platform (GCP)
 </li>
 <li>
 Familiarity with specific Agentic AI frameworks such as Google Cloud ADK (Agent Development Kit), CrewAI, LangChain etc
 </li>
 <li>
 Experience with MLOps principles and tools for model deployment and monitoring
 </li>
 <li>
 Knowledge of techniques for grounding generative models with external knowledge sources (e.g., Retrieval-Augmented Generation - RAG, Knowledge Graphs)
 </li>
 <li>
 Experience with multi-agent systems and orchestration
 </li>
 </ul>
 
 
 Get to know us
 
 
 
 If you want to know more about us and what we do, then visit our website: www.oredata.com
 
 
 
 Why Oredata?
 
 
 <ul>
 <li>
 Remote working and flexible time off
 </li>
 <li>
 Open communication, flexibility and start-up spirit
 </li>
 <li>
 Learning &amp; Development opportunities for both personal and professional growth
 </li>
 <li>
 Opportunity to get company paid Professional Certificates (Google Cloud Platform, Confluent Kafka, etc)
 </li>
 <li>
 Access to Online Training Platforms (Udemy, Pluralsight, A Cloud Guru, Coursera, etc.)
 </li>
 <li>
 Dynamic work ecosystem where you can take initiative and responsibility
 </li>
 <li>
 Opportunity to work on international projects
 </li>
 <li>
 Private Health Insurance
 </li>
 <li>
 Birthday Leave Policy
 </li>
 
 </ul>
 
 
 Ki&scedil;isel verileriniz i&scedil;e al&inodot;m s&uuml;recinin y&uuml;r&uuml;t&uuml;lebilmesi amac&inodot;yla veri sorumlusu s&inodot;fat&inodot;yla &scedil;irketimiz Oredata Yaz&inodot;l&inodot;m A.&Scedil;. taraf&inodot;ndan i&scedil;lenecektir.
 
 Ki&scedil;isel verilerinizin i&scedil;lenmesi ve haklar&inodot;n&inodot;zla ilgili detayl&inodot; bilgiye https://oredata.com/personal-data-protection-policy/ ba&gbreve;lant&inodot;s&inodot; &uuml;zerinden ula&scedil;abilirsiniz.
 
 
 
</div>

Agentic AI Engineer

digital.enterprises

<div>
 
 
 BUILD THE REAL-TIME INTELLIGENCE OF ADAPT
 
 
 
 
 
 
 
 About digital.enterprises
 
 
 
 digital.enterprises is shaping the future of the energy sector. We help companies run smarter integrating people, data, and technology to create intelligent, adaptive, and human-centred operations. Based in Doha, the LNG and GTL capital of the world, we fuse deep industry expertise with cutting-edge digital thinking to transform how energy companies work, connect, and grow. If you are driven by impact, speed, and purpose not titles or bureaucracy you will fit right in.
 
 
 
 
 
 
 About ADAPT
 
 
 
 ADAPT is our flagship platform a real-time digital mesh connecting the energy sectors Maintenance, Repair &amp; Operations (MRO) ecosystem. Instead of long procurement cycles, ADAPT matches buyer demand with supplier capability instantly, using live data and AI to surface qualified options with governance, auditability, and human-in-the-loop controls.
 
 
 
 
 
 
 About the Role
 
 
 
 We are seeking an experienced AI Engineer to architect and implement the intelligent layer powering ADAPT. You will build production-grade LLM applications that disintermediate RFQs through real-time matching, provide real-time decision support, and deliver actionable insights to both buyers and suppliers. This role requires deep technical expertise in modern AI frameworks, enterprise-grade system design, and the ability to translate complex business requirements into reliable AI solutions.
 
 
 
 
 
 
 Key Responsibilities
 
 
 
 
 
 
 
 - AI Application Development
 
 
 <ul>
 <li>
 Design and deploy production LLM applications (LangChain/LangGraph) for supplier matching, requirement extraction, and automated workflow orchestration.
 </li>
 <li>
 Implement sophisticated tool/function calling to integrate LLMs with external systems (inventory databases, ERPs, communications platforms).
 </li>
 <li>
 Build robust context and memory systems for multi-turn conversations and long-running industrial processes.
 </li>
 <li>
 Develop intelligent agents for supplier evaluation, alternative proposals, and emergency response scenarios.
 </li>
 </ul>
 
 
 
 
 
 - System Architecture &amp; Optimisation
 
 
 <ul>
 <li>
 Write clean, maintainable code following established patterns and best practice.
 </li>
 <li>
 Optimise inference pipelines for latency, cost, and accuracy in high-stakes environments.
 </li>
 <li>
 Design scalable architectures for concurrent users across maintenance, procurement, and supplier teams.
 </li>
 <li>
 Implement caching, batching, and performance optimisations for enterprise-scale deployments.
 </li>
 </ul>
 
 
 
 
 
 - AI Quality &amp; Reliability
 
 
 <ul>
 <li>
 Develop comprehensive evaluation frameworks (AI evals) to measure accuracy, relevance, and business impact.
 </li>
 <li>
 Design and execute fine-tuning strategies for domain-specific models (MRO terminology, supplier capability, part specifications).
 </li>
 <li>
 Explore and implement RL-based agents for dynamic supplier analysis where beneficial.
 </li>
 <li>
 Create sophisticated prompt engineering solutions that balance specificity and flexibility.
 </li>
 </ul>
 
 
 
 
 
 - Security &amp; Compliance
 
 
 <ul>
 <li>
 Implement security best practice for sensitive procurement data, supplier information, and commercial terms.
 </li>
 <li>
 Ensure compliance with data protection regulations and industry standards (ISO, API standards, etc.).
 </li>
 <li>
 Design audit trails and logging for AI-assisted decisions in regulated environments.
 </li>
 <li>
 Build safeguards against prompt injection, data leakage, and other AI-specific vulnerabilities.
 </li>
 </ul>
 
 
 
 
 
 - Domain Integration
 
 
 <ul>
 <li>
 Collaborate with domain experts to understand oil &amp; gas MRO workflows, terminology, and critical requirements.
 </li>
 <li>
 Travel to client sites, supplier locations, and operational facilities to understand real-world constraints.
 </li>
 <li>
 Translate operational requirements into technical specifications and AI capabilities.
 </li>
 <li>
 Provide technical guidance during customer implementations and onboarding.
 </li>
 </ul>
 
 
 
 
 
 Required Qualifications
 
 
 
 
 
 
 
 - Experience &amp; Background
 
 
 <ul>
 <li>
 5+ years of professional software engineering experience in enterprise environments.
 </li>
 <li>
 Hands-on experience delivering working AI applications in production or advanced pilot environments.
 </li>
 <li>
 Experience working in regulated or sensitive-data settings.
 </li>
 </ul>
 
 
 
 
 
 Technical Expertise
 
 
 
 
 
 
 
 - LLM Frameworks &amp; Foundation Models
 
 
 <ul>
 <li>
 Deep expertise building complex agent workflows.
 </li>
 <li>
 Hands-on with major LLMs (OpenAI, Anthropic, open-source) including API integration and optimisation.
 </li>
 </ul>
 
 
 
 
 
 - Advanced AI Patterns
 
 
 <ul>
 <li>
 Function/tool calling and external system integration.
 </li>
 <li>
 Context-window management and retrieval strategies.
 </li>
 <li>
 Memory systems for stateful conversations and long-running processes.
 </li>
 </ul>
 
 
 
 
 
 - AI Engineering
 
 
 <ul>
 <li>
 Building evaluation frameworks and metrics for LLM applications.
 </li>
 <li>
 Fine-tuning for domain-specific use cases.
 </li>
 <li>
 Experimenting with RL-based agents where impactful.
 </li>
 <li>
 Advanced prompt engineering and optimisation.
 </li>
 </ul>
 
 
 
 
 
 - Software Engineering
 
 
 <ul>
 <li>
 Clean-code principles and&nbsp;SOLID&nbsp;design patterns.
 </li>
 <li>
 Performance optimisation and scalability.
 </li>
 <li>
 Version control (Git) and&nbsp;CI/CD&nbsp;pipelines.
 </li>
 <li>
 Testing strategies for AI systems (unit, integration, end-to-end).
 </li>
 <li>
 Security best practice, data protection, and compliance awareness.
 </li>
 </ul>
 
 
 
 
 
 - Soft Skills
 
 
 <ul>
 <li>
 Communication: explain complex AI concepts to non-technical stakeholders; write clear documentation; collaborate across teams.
 </li>
 <li>
 Travel: willingness and ability to travel to operational sites, client facilities, and partner locations.
 </li>
 <li>
 Problem-solving: strong analytical thinking; break down complex business problems into implementable AI solutions.
 </li>
 </ul>
 
 
 
 
 
 - Preferred
 
 
 <ul>
 <li>
 Domain knowledge in oil &amp; gas operations, supply chain, or industrial procurement.
 </li>
 <li>
 Experience with MRO/EAM/supply chain software.
 </li>
 <li>
 Familiarity with industry-specific standards and terminology.
 </li>
 <li>
 Background in traditional ML/data science beyond LLMs.
 </li>
 <li>
 Experience with vector databases, semantic search, and RAG architectures.
 </li>
 </ul>
 
 
 
 
 
 Why join us?
 
 
 
 At digital.enterprises, AI is a partner to people, not a replacement. This is Industry 5.0 in practice: technology amplifying human capability so energy operations become safer, more reliable, and more efficient delivering secure, affordable, cleaner energy at scale. You will join a mission-driven team led by credible industry leadership with the freedom to build technology that genuinely changes how an industry works.
 
</div>

Artificial Intelligence Engineer

TriDevSofts

We're Hiring: GCP AI Architect (Contractual Role) (Remote)Are you passionate about building intelligent solutions on Google Cloud Platform? We're looking for a skilled GCP AI Architect to join our team and lead the design and deployment of cutting-edge AI/ML systems.What You'll Do:<ul><li>Architect scalable AI/ML solutions using GCP services like Vertex AI, BigQuery, and TensorFlow</li><li>Collaborate with data scientists and engineers to bring models into production</li><li>Ensure best practices in cloud security, performance, and cost optimization</li></ul>What We're Looking For:<ul><li>Proven experience with GCP AI/ML tools and architecture</li><li>Strong understanding of data pipelines, model lifecycle, and MLOps</li><li>Excellent communication and problem-solving skills</li></ul>Note: This is a contractual position &mdash; only candidates interested in contract-based work should apply.Job Types: Contractual / Temporary, Freelance Contract length: 6 monthsWork Location: Remote

GCP AI Architect

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