ai engineer

backend engineer

quality engineer

full stack engineer

head of engineering

back end engineer

Sundus

<div>
 
 Job Title
 
 : Backend Engineer
 
 
 
 Job Code
 
 : JPC 6311
 
 
 
 Experience: min 7 years
 
 
 
 
 Education:
 
 Bachelor s degree in Computer Science, Engineering, or related field.
 
 
 
 Language Skills
 
 : English
 
 
 
 Role Purpose
 
 
 
 The Backend Engineer is responsible for designing, developing, and maintaining
 
 
 scalable, secure, and high-performance backend systems that power AI-driven
 
 
 applications. This role plays a critical part in building robust APIs, integrating AI/ML
 
 
 services, and ensuring reliable data processing and system performance in production
 
 
 environments.
 
 
 
 Required Skills And Qualifications
 
 
 
 Bachelor s degree in Computer Science, Engineering, or related field.
 
 
 7+ years of experience in backend development.
 
 
 Strong programming expertise in Python, Java, Node.js etc.
 
 
 Proven experience building scalable APIs and microservices architectures.
 
 
 Strong knowledge of databases (SQL and NoSQL) such as PostgreSQL,
 
 
 MySQL, MongoDB, or Cosmos DB.
 
 
 Experience with cloud platforms (Azure, AWS or GCP).
 
 
 Solid understanding of distributed systems, caching, and asynchronous
 
 
 processing.
 
 
 Experience with Git and version control workflows.
 
 
 Strong debugging and problem-solving skills.
</div>

Backend Engineer

NorthBay Solutions

<div><h1 class="jobSectionHeader">Full Stack Engineer</h1>
 Location: Abu Dhabi, UAE 
Employment Type: Full-time<h2 class="jobSectionHeader">
 Job Summary</h2>
NorthBay is seeking a Full Stack Engineer with strong expertise in modern front-end and backend technologies, particularly JavaScript/TypeScript, React, Node.js, and Nest.js. The role involves designing, developing, and maintaining scalable full-stack applications for AI-driven platforms, with a strong focus on performance, clean architecture, maintainability, security, and seamless user experience.
The ideal candidate will have hands-on experience building responsive front-end applications and robust backend services using Node.js/Nest.js, developing RESTful APIs, integrating databases and third-party services, and collaborating closely with ML engineers, backend developers, and product/design teams.
Experience working with AI/LLM platforms and AI API integrations is highly desirable.<h2 class="jobSectionHeader">
Key Responsibilities</h2> 
 Design, develop, and maintain scalable full-stack web applications using React, Next.js, TypeScript, Node.js, and Nest.js.<ul><li>Build responsive, accessible, and high-performance front-end applications using modern frameworks and reusable components.</li><li>Develop scalable and secure backend services, APIs, and microservices using Node.js and Nest.js.</li><li>Design and implement RESTful APIs and integrate third-party services and AI APIs.</li><li>Work with databases and data-access layers, ensuring efficient queries, data integrity, and scalability.</li><li>Implement authentication, authorization, security controls, and role-based access where required.</li><li>Collaborate closely with ML engineers, product managers, designers, and other engineering teams to deliver intuitive AI-powered applications.</li><li>Integrate AI/LLM capabilities into web applications, including real-time model outputs and AI-powered workflows.</li><li>Optimize application performance across both front-end and backend layers.</li><li>Build reusable components, services, and design patterns that improve maintainability and development efficiency.</li><li>Participate in architectural discussions, code reviews, technical planning, and engineering best practices.</li><li>Contribute to automated testing, documentation, CI/CD pipelines, and deployment processes.</li><li>Troubleshoot production issues and identify opportunities for improving application reliability, scalability, and performance.</li><li>Mentor junior engineers and contribute to a collaborative engineering culture.</li></ul><h2 class="jobSectionHeader">
Required Skills &amp; Qualifications</h2><ul><li> 
5+ years of full-stack development experience with strong command of JavaScript and TypeScript.</li><li>Strong hands-on experience with React and modern front-end frameworks such as Next.js, Vue.js, or similar.</li><li>Strong backend development experience with Node.js and Nest.js.</li><li>Experience designing and developing RESTful APIs and backend services.</li><li>Strong understanding of backend architecture, API design, authentication, authorization, and security best practices.</li><li>Experience working with relational and/or NoSQL databases such as PostgreSQL, MySQL, MongoDB, or similar.</li></ul> 

VdJnUXaMRL</div>

Full Stack Engineer

Salient Group

<div>
 
 
 AI/ML Engineer (Mid, Senior, Lead)
 
 
 
 
 Location
 
 | Abu Dhabi/Dubai (Hybrid)
 
 
 
 Comp
 
 | $200k-$250k + Equity + Package
 
 
 
 Focus
 
 | Real-World AI Deployment Across Enterprise &amp; Operational Environments, AI Systems, LLM Applications &amp; Agentic Workflows
 
 
 
 
 
 We&rsquo;re working with a frontier AI company that builds and deploys AI systems for the world&rsquo;s most important institutions, automating billion-dollar processes, unlocking national productivity, and modernising the infrastructure of society itself. This is not research for research&rsquo;s sake. This is applied AI, deployed in the real world.
 
 
 
 
 
 They are backed by some of the world&rsquo;s most respected founders and AI pioneers, and built a 70+ person team from tier-1 companies. Now, they&rsquo;re expanding globally and looking for engineers who want to own the deployment of AI that changes how the world works.
 
 
 
 
 
 
 The Role
 
 
 
 As an AI/ML Engineer, you&rsquo;ll turn state-of-the-art models into production-grade systems that power national-scale change. You&rsquo;ll design, deploy, and optimise AI applications that automate some of the hardest real-world processes from energy and healthcare to government operations.
 
 
 
 
 
 You&rsquo;ll be embedded in small, elite teams that own problems end-to-end: architecture, data, model design, deployment, and iteration. No silos. No bureaucracy. Just impact.
 
 
 
 
 
 
 What You&rsquo;ll Do
 
 
 <ul>
 <li>
 Build and deploy applied AI systems: from LLMs and agentic reasoning models to scalable data pipelines and production infrastructure.
 </li>
 <li>
 Solve world-scale problems: automate complex workflows, improve public outcomes, and optimise critical national systems.
 </li>
 <li>
 Collaborate with leaders and clients: work directly with government officials, enterprise executives, and industry veterans to translate ideas into deployed solutions.
 </li>
 <li>
 Operate like a founder: own delivery, make decisions fast, and help shape the next generation of applied AI tooling.
 </li>
 <li>
 Stay ahead of the curve: explore the latest GenAI, LLM, and reasoning research to push models from theory to practice.
 </li>
 </ul>
 
 
 
 
 
 You&rsquo;ll Thrive Here If You&hellip;
 
 
 <ul>
 <li>
 Have hands-on experience building GenAI or LLM-based applications (agents, RAG, reasoning models).
 </li>
 <li>
 Know your way around Python, PyTorch, JAX, or TensorFlow, with strong software engineering fundamentals.
 </li>
 <li>
 Can work across data pipelines, APIs, and production deployments with speed and precision.
 </li>
 <li>
 Enjoy autonomy, creative problem-solving, and owning solutions end-to-end.
 </li>
 <li>
 Want your work to matter to see it deployed across industries, governments, and communities.
 </li>
 </ul>
 
 
 
 
 
 Why Join
 
 
 <ul>
 <li>
 Build at frontier scale: ship real systems used by governments, hospitals, and energy leaders worldwide.
 </li>
 <li>
 Elite peers: collaborate with engineers and researchers from tier-1 places.
 </li>
 <li>
 Move fast, own outcomes: you&rsquo;ll ship code in weeks, not quarters.
 </li>
 <li>
 Meaningful rewards: competitive comp, equity, and ownership tied to real impact.
 </li>
 <li>
 Support to thrive: daily lunches, full benefits, 401(k), and unlimited PTO.
 </li>
 </ul>
 
 
 
 
 If you want to build AI that doesn&rsquo;t just predict but powers economies, saves lives, and redefines what&rsquo;s possible , this is where you do it.
 
 
 
 
 
 Join the team building AI that actually ships. Get in touch with danny@salientgroup.com.au for more information.
 
</div>

Artificial Intelligence Engineer

Brain Co.

<div>
 Our Mission
 
 
 Rebuild how the world works, to make institutions work better for the people they serve.
 
 
 
 About Brain Co.
 
 
 
 Brain Co. builds AI-native operating systems for large, regulated institutions. Each system is built for a specific industry, powered by agents that push real workflows forward. Underneath it all is Atlas, our proprietary platform that keeps customers in control, secure by design, and never locked into one model.
 
 
 Why Now
 
 
 Brain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services.
 
 
 Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact.
 
 
 You'll work alongside exceptional peers on some of the hardest problems in applied AI. It&rsquo;s the kind of work you'll still be proud of in ten years from now.
 
 
 
 Machine Learning Engineer, Applied AI
 
 
 
 
 About The Role
 
 
 
 So much of the work society depends on is still slower and harder than it should be. Permits take months. Claims sit unresolved. And AI hasn't changed that &mdash; because the bottleneck isn't the models. It's the institutional context AI needs to do the work: rules, history, relationships, and judgment scattered across people, documents, and legacy systems.
 
 
 BrainCo exists to fix that. We build agent-native operating systems for the institutions society depends on, and our products are the first of their kind in the world &mdash; we were the first, anywhere, to fully automate construction permitting, and we're now doing the same across insurance and other industries. There is no playbook here, because no one has built this before.
 
 
 As a Machine Learning Engineer on Applied AI, your work begins where the demo ends: getting a model to look impressive is the easy part; making it a production decision system an institution stakes its process on is the job. The problems come in every shape &mdash; custom vision model pipelines that check blueprints against building codes at 95%+ accuracy, agents that untangle policy stacks to reveal coverage gaps, systems that predict from clinical records whether a patient is on their care path &mdash; and you'll own them end-to-end, from ambiguous customer problem to the eval that catches a whole class of errors.
 
 
 This is frontier ML applied where it's hardest and matters most. The problems are underspecified, the documents are brutal, the accuracy bar is institutional-grade &mdash; and the feedback loops are real, because our systems move real workflows forward every day.
 
 
 
 Who We're Looking For
 
 
 
 You understand how machine learning actually works &mdash; not just the tooling, but the philosophy underneath: what a loss function really optimizes, how generalization breaks under distribution shift, why evaluation is where systems quietly go wrong. And you live at the bleeding edge of modern AI, with hard-won instincts for squeezing the most out of LLMs and agentic systems &mdash; prompting, fine-tuning, tool use, and reasoning. That combination is the job: you know when a fine-tuned segmentation model beats a VLM, when a rule engine beats both, and how to compose all three into a system more accurate than any single model. You treat frontier models as components to be measured, pushed, and engineered &mdash; never as magic.
 
 
 Most of all, you're energized by building things that have never existed, and comfortable when the problem, the data, and the definition of success all have to be invented at once.
 
 
 
 The Problems You'll Work On
 
 
 
 
 Composite AI systems and credit assignment.
 
 Our most demanding systems chain vision transformers, segmentation models, VLM reasoning, and rule engines. When the pipeline is wrong, which component failed? One of the most interesting open problems in applied ML.
 
 
 
 Document understanding beyond the frontier.
 
 Blueprints, site plans, policy stacks, contracts, clinical records &mdash; dense, multimodal documents that break off-the-shelf models. You'll build models that actually read them.
 
 
 
 Agents that learn from real work.
 
 Our deployments generate verified, ground-truth outcomes on every decision &mdash; reward signals most labs can only simulate. You'll help design the data, evals, and training loops to build and fine-tune agents on them.
 
 
 
 Evaluation as a product discipline.
 
 When a regulator has to trust your system, evals are the product. You'll build eval suites and failure-mode taxonomies rigorous enough to earn institutional sign-off.
 
 
 
 Institutional Intelligence that compounds.
 
 Every verified correction improves the system twice: the corrected fact percolates to every application, and the system that builds the intelligence learns to build it better. You'll work on both loops.
 
 
 
 In This Role, You Will
 
 
 
 
 Turn ambiguity into shipped systems
 
 &mdash; from no problem statement, no labeled data, and no agreed definition of success, to well-posed ML problems and production deployments.
 
 
 
 Own AI systems end-to-end.
 
 There is no handoff: the person who trains the model owns its behavior in production.
 
 
 
 Work at the research frontier with production stakes
 
 , applying LLMs, RL fine-tuning, and agentic systems where the output is a decision an institution acts on.
 
 
 
 Work directly with the institutions we serve
 
 &mdash; permit reviewers, underwriters, compliance officers &mdash; to understand how decisions actually get made and ensure your systems change how the work gets done.
 
 
 
 Engineer for production reality
 
 , navigating accuracy, latency, cost, and reliability in environments far messier than any benchmark.
 
 
 
 Raise the bar across the company
 
 through design reviews, our internal paper club, and the shared playbook for AI systems institutions can trust.
</div>

Machine Learning Engineer, Applied AI - Deployed

Inception42

<div>
 
 
 MLOps Engineer
 
 
 
 
 Location:
 
 Abu Dhabi, UAE
 
 
 
 Employment type:
 
 Full-time
 
 
 
 
 
 Inception42, a G42 company, is the region&rsquo;s leading innovator of AI-powered domain-specific as well as industry-agnostic products, built on a rich heritage of research and development. Within the G42 ecosystem, Inception42 functions as the core intelligence layer &ndash; transforming data and compute infrastructure into real-world, applied AI solutions. Beyond its commercial endeavors, Inception42 is committed to creating positive societal impact. For more information, please visit www.inceptionai.ai
 
 
 
 
 
 
 Overview
 
 
 
 We are looking for an MLOps Engineer to build and operate the systems that move machine learning from development into reliable production use. You will work across model training, validation, packaging, deployment, observability, and infrastructure automation, creating repeatable workflows that improve delivery speed without compromising security or control. The role works closely with data scientists, ML engineers, software engineers, architects, product teams, and platform teams in an environment where production quality, scale, and practical deployment matter.
 
 
 
 
 
 
 What You&rsquo;ll Own
 
 
 
 Design, build, and operate end-to-end ML pipelines covering data preparation, training, validation, packaging, release, deployment, and production monitoring.
 
 
 Build CI/CD workflows for machine learning systems, with automated testing, quality gates, environment promotion, release controls, rollback, and reproducible builds.
 
 
 Own model lifecycle controls across experiment tracking, model and dataset versioning, registries, approvals, deployment governance, lineage, and production-readiness checks.
 
 
 Deploy and operate model-serving and inference workloads across production and non-production environments using containerised, cloud-native platforms.
 
 
 Create reusable platform components, templates, standards, and paved paths that reduce manual work and help teams move models from research to production consistently.
 
 
 Instrument ML systems for model quality, data and concept drift, latency, throughput, availability, infrastructure health, logs, traces, alerts, and service-level performance.
 
 
 Automate infrastructure and environment provisioning through infrastructure as code, configuration management, and disciplined controls for access, secrets, networking, storage, and compute.
 
 
 Debug and optimise pipelines, model-serving paths, and platform services at component and system level to improve reliability, scalability, performance, and cost efficiency.
 
 
 Embed security, compliance, access control, auditability, and data-governance requirements into ML platforms, automated workflows, and deployment processes.
 
 
 Collaborate closely with data science, engineering, product, and platform teams to resolve production issues, improve developer experience, document operating practices, and evolve the MLOps capability.
 
 
 
 
 
 
 What We&rsquo;re Looking For
 
 
 
 Strong engineering fundamentals across software delivery, machine learning systems, cloud infrastructure, automation, reliability, and production operations.
 
 
 A track record of building and operating production ML or data platforms, with clear ownership of deployment quality, reliability, security, and operational outcomes.
 
 
 Hands-on experience building CI/CD pipelines and automation using Python and shell scripting or comparable languages and tooling.
 
 
 Practical experience with containers and orchestration, including Docker and Kubernetes, and the ability to diagnose issues across applications, clusters, networks, storage, and compute.
 
 
 Working knowledge of Azure services for ML and application workloads, such as Azure Machine Learning, Azure AI Foundry, AKS, Azure DevOps, storage, networking, and managed data services, or equivalent cloud platforms.
 
 
 Experience with ML lifecycle and workflow capabilities such as experiment tracking, model registries, feature and dataset versioning, orchestration, and model serving using MLflow, Kubeflow, Airflow, DVC, or equivalent tools.
 
 
 Strong observability and debugging skills across model behaviour, data quality, drift, inference performance, distributed services, and infrastructure health.
 
 
 Clear communication and product judgement, with the ability to work through ambiguity, make pragmatic trade-offs, and collaborate effectively across research, engineering, platform, and product teams.
 
 
 
 
 
 
 Nice to Have
 
 
 
 Experience deploying generative AI or LLM workloads, including prompt and model versioning, evaluation, guardrails, GPU environments, or high-performance inference.
 
 
 Exposure to distributed data platforms, search systems, SQL or NoSQL databases, time-series storage, feature stores, or large-scale training environments.
 
 
 Experience with Terraform or Bicep and observability platforms such as Prometheus, Grafana, ELK, Datadog, or Langfuse.
 
</div>

MLOps Engineer

DICETEK LLC

<div>Description:
We are looking for an experienced AI Engineer responsible for designing, developing, and deploying Generative AI and Agentic AI solutions that support enterprise-scale business use cases. This role includes building intelligent systems that integrate complex backend services and client-facing applications across web, mobile, and enterprise platforms.
The primary responsibility will be to design and develop AI-powered applications, autonomous agents, and multi-agent workflows, while coordinating with cross-functional teams across architecture, engineering, product, and business functions. Thus, a commitment to collaborative problem solving, sophisticated design, and product quality is essential.
KEY ACCOUNTABILITIES:<ul><li>
Designing and building Generative AI applications using Large Language Models (LLMs)</li><li>
Developing Agentic AI solutions, including autonomous agents, multi-agent orchestration, and workflow-driven decision systems</li><li>
Building solutions using frameworks such as LangChain, LangGraph or similar agent frameworks</li><li>
Implementing RAG architecture with vector databases</li><li>
Knowledge of embeddings, vector DBs, and model evaluation.</li><li>
Implement various Context Engineering strategies to reduce Token Utilization &amp; Latency.</li><li>
Implement RAG Strategies to reduce retrieval latency and answer relevancy.</li><li>
Prompt engineering, tool calling, memory management, and agent orchestration</li><li>
Integrating AI services with enterprise APIs, middle ware, and backend systems</li><li>
Evaluated &amp; Improvise AI Agent Performance using several metrics</li><li>
Deploying scalable AI solutions on Azure / AWS cloud platforms</li><li>
Working with model evaluation, guardrails, observability, and AI governance</li></ul>
QUALIFICATIONS &amp; EXPERIENCE:<ul><li>
Bachelor&rsquo;s degree in computer science, Engineering, or a related field.</li><li>
Senior professional with around 10 years of experience.</li><li>
3+ years of relevant experience in AI Engineering</li><li>
Working experience in an agile software development environment with a good understanding of the principles of agile architecture. Strong collaborative mindset for collective decentralized decision making.</li><li>
Demonstrate strong technical skills with a deep understanding of modern architectural styles and practices such as Microservices, Containers, Cloud (AWS, Azure), APIs, Continuous Delivery, Event-driven architecture, Evolutionary architecture, etc., with a passion for hands on coding.</li><li>
Strong foundation knowledge of software architecture concepts, patterns, principles, and quality attributes. Ability to consistently apply them in real-world scenarios with a pragmatic, system thinking, and problem-solving mindset by analyzing architecture trade-offs for delivering high-quality, sustainable solution architecture.</li><li>
Proven leadership skills with a proactive, positive, and growth mindset. Ability to foster and motivate programmers for delivering with craftsmanship. Good personal skills to continuously engage and communicate with an egoless empathetic mindset.</li><li>
Experience and expertise in delivering architecture for large software solutions meeting critical business purposes. Ability to proactively discover technical debts and continuous improvement opportunities of existing live systems. Work closely with the product owner and enterprise architects to influence and prioritize technical backlog items.</li><li>
Strong proficiency in Python (NumPy, pandas, FastAPI, PyTorch/TensorFlow).</li><li>
Experience with Agent framework LangChain, Lang Graph.</li><li>
Architect end-to-end data indexing pipelines optimized for semantic search and RAG.</li><li>
Design resilient data ingestion frameworks that ensure high availability and low latency for vectorized datasets.</li><li>
Experience deploying and managing models via Amazon Bedrock, Azure OpenAI Service, and Google Vertex AI.</li><li>
Integration of pre-built AI APIs for Speech-to-Text, Computer Vision, and Natural Language Processing (NLP).</li><li>
Leveraging serverless architectures (e.g., AWS Lambda, Azure Functions) to trigger AI inference and data pre-processing.</li><li>
Experience working with CI/CD tools like Jenkins/Gitlab in Cloud Native environments.</li><li>
Experience in setting up pipeline for cloud based and on-prem based application with static code analysis, requirement tagging in Jira.</li><li>
Experience working with one or more DevOps tools &amp; config management tools.</li><li>
Experience in managing and deploying AI workloads in Kubernetes.</li><li>
Experience operating monitoring tools for traditional and cloud environments.</li><li>
Strong analytical mind for problem solving.</li></ul></div>

Senior AI Engineer

<div>JOB PURPOSE:
We are looking for an experienced AI Architect responsible for architecting enterprise-wide Generative AI and Agentic AI capabilities across banking systems. The role will define target architecture, integration patterns, standards, reference implementations and reusable building blocks for full-fledged AI chat assistants, autonomous agents and multi-agent workflows.
The AI Architect will lead end-to-end architecture for agentic retail banking journeys such as payments, transfers, servicing, self-service fulfilment and customer assistance, ensuring secure integration with enterprise APIs, middleware, core banking platforms and customer-facing channels across web and mobile.
This role requires deep hands-on AI engineering capability, strong banking domain understanding, practical delivery experience with multiple production-grade banking agents, and the ability to collaborate with product, engineering, infrastructure, cybersecurity, data, governance and enterprise architecture teams to present and align designs through ARB. 
QUALIFICATIONS &amp; EXPERIENCE:
Minimum Qualification<ul><li>Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, Data Science or a related discipline.</li><li>Relevant certifications in cloud architecture, AI engineering, security architecture, enterprise architecture or machine learning are preferred.</li></ul> 
Minimum Experience<ul><li>Senior technology professional with around 12+ years of experience across software engineering, architecture, cloud/platform engineering and enterprise solution delivery.</li><li>Minimum 4+ years of hands-on AI engineering or AI architecture experience, including Generative AI, LLM applications and Agentic AI solutions.</li><li>Proven experience architecting and delivering multiple banking agents or full-fledged banking chat assistant capabilities integrated with enterprise systems.</li><li>Strong understanding of banking systems, retail banking journeys, payments, transfers, servicing, customer self-service, operational controls and regulatory/security considerations.</li><li>Full AI Engineer capability including Python, API integration, microservices, event-driven design, RAG implementation, model/agent evaluation and cloud-native deployment practices.</li><li>Hands-on experience with agent frameworks and orchestration platforms such as Microsoft Semantic Kernel, AutoGen, LangChain, LangGraph and similar frameworks.</li><li>Experience architecting MCP servers, tool integration layers, agent-to-agent communication, UI integration patterns and agent interoperability protocols such as MCP, A2A and A2UI.</li><li>Strong experience with Azure AI services, Azure OpenAI Service, AWS AI services, Amazon Bedrock and related model deployment/management capabilities.</li><li>Experience designing secure AI systems with zero trust principles, identity and access controls, data protection, secure API design, PII redaction and privacy-by-design controls.</li><li>Experience presenting solution architecture, trade-off analysis, ADRs and architecture recommendations to ARB or equivalent architecture governance forums.</li><li>Experience recommending infrastructure architecture for AI platforms including compute, Kubernetes, serverless, vector databases, observability, monitoring, data pipelines and connectivity.</li><li>Strong capability to collaborate with engineering, product, cybersecurity, infrastructure, operations, data, compliance and enterprise architecture teams.</li></ul> 
Key Technical Skills<ul><li>Enterprise Agentic AI architecture, multi-agent systems, autonomous workflows, human-in-the-loop design and full-fledged chat assistant architecture.</li><li>LLMs, prompt engineering, context engineering, memory design, tool/function calling, agent orchestration, model/agent evaluation and cost/latency optimization.</li><li>RAG architecture, semantic indexing, embeddings, vector databases, retrieval optimization, reranking, grounding, answer relevancy and explainability patterns.</li><li>MCP server architecture, tool registries, multi-tool integration, A2A, A2UI, agent interoperability protocols and AI ecosystem design.</li><li>Azure AI, Azure OpenAI, AWS AI services, Amazon Bedrock, Kubernetes, serverless, microservices, APIs, event-driven architecture and observability.</li><li>Security architecture for AI systems including zero trust, PII redaction, data masking, privacy controls, guardrails, secure logging and auditability.</li></ul> 
Behavioural / Leadership Skills<ul><li>Strategic architecture thinking with the ability to define enterprise standards, influence platform direction and simplify complex technical decisions.</li><li>Strong stakeholder communication with the ability to present architecture options, risks, trade-offs and recommendations to senior leadership and ARB forums.</li><li>Collaborative leadership style with the ability to work across business, product, engineering, cybersecurity, data and infrastructure teams.</li><li>Hands-on problem-solving mindset, pragmatic decision making, ownership, mentoring capability and commitment to high-quality secure delivery.</li></ul> 
Technical Competencies<ul><li>Enterprise Agentic AI architecture and banking-grade AI ecosystem design.</li><li>Retail banking agent architecture for payments, transfers, servicing and self-service workflows.</li><li>RAG, memory, context engineering, evaluation, tool orchestration and MCP server architecture.</li><li>AI security architecture, zero trust, PII redaction, guardrails, auditability and governance.</li><li>Azure and AWS AI services, cloud-native infrastructure, Kubernetes, serverless and observability for agent platforms.</li><li>Architecture documentation, ADR creation, ARB presentation and cross-team design governance.</li></ul> 
Skills: AI Architecture , LLMs , Azure AI</div>

AI Architect – Enterprise Agentic AI Architecture

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