it support engineer

sales engineer

technical sales engineer

ai engineer

applied ai engineer

ai solutions engineer

<div>
 <p>
  <strong>
   Forward Deployed Engineer
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  We are looking for a Forward Deployed Engineer to join our Team and work directly with operating companies across the Group to turn real-world operational challenges into deployed AI solutions.
 </p>
 <p>
  This is a hands-on engineering role for someone who enjoys working closely with business users, understanding how work is performed, building practical AI solutions and taking them all the way from discovery to production and handover.
 </p>
 <p>
  The successful candidate will combine software engineering, AI/LLM development, systems integration and business problem-solving to deliver solutions that create measurable operational value.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Key Responsibilities
  </strong>
 </p>
 <ul>
  <li>
   Work directly with operating companies to identify operational problems and translate them into practical AI and automation solutions.
  </li>
  <li>
   Conduct process discovery, requirements gathering and stakeholder interviews.
  </li>
  <li>
   Design, build, test and deploy AI-powered solutions using internal platforms, models and reusable components.
  </li>
  <li>
   Develop solutions using
   <strong>
    Python, LLM APIs, RAG, vector databases, agent frameworks and workflow orchestration
   </strong>
   .
  </li>
  <li>
   Integrate AI solutions with existing operational systems, APIs, data sources and business workflows.
  </li>
  <li>
   Rapidly prototype solutions, test them with real users and iterate towards production-ready implementations.
  </li>
  <li>
   Develop evaluation and testing frameworks to measure AI solution quality and performance.
  </li>
  <li>
   Monitor deployed solutions in production and troubleshoot performance, quality and integration issues.
  </li>
  <li>
   Work with Group IT, Information Security, Data &amp; Analytics and business teams to ensure appropriate architecture, security and compliance.
  </li>
  <li>
   Document solutions and prepare technical handover materials.
  </li>
  <li>
   Train business users and operational owners to use, maintain and extend deployed solutions.
  </li>
  <li>
   Track and report measurable benefits from each deployment, including cost savings, productivity improvements and licence displacement.
  </li>
  <li>
   Identify reusable components and patterns that can accelerate future AI deployments across the Group.
  </li>
  <li>
   Support build-versus-buy assessments by demonstrating the technical feasibility and total cost of internally developed solutions.
  </li>
  <li>
   Where required, lead temporary delivery pods or coordinate the work of interns.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Requirements
  </strong>
 </p>
 <ul>
  <li>
   Bachelor's degree in
   <strong>
    Artificial Intelligence, Computer Science, Software Engineering, Data Science, Industrial Engineering or a related discipline
   </strong>
   .
  </li>
  <li>
   <strong>
    1&ndash;3 years of hands-on experience
   </strong>
   building and deploying software or AI solutions into production.
  </li>
  <li>
   Proven experience delivering solutions used by real users beyond proof-of-concept or experimental environments.
  </li>
  <li>
   Recent practical experience developing applications using
   <strong>
    Large Language Models (LLMs)
   </strong>
   is required.
  </li>
  <li>
   Strong experience with
   <strong>
    Python
   </strong>
   for application development and/or data-related work.
  </li>
  <li>
   Experience working with
   <strong>
    LLM APIs, prompt engineering and context design
   </strong>
   .
  </li>
  <li>
   Practical knowledge of
   <strong>
    Retrieval-Augmented Generation (RAG)
   </strong>
   and vector databases.
  </li>
  <li>
   Experience with
   <strong>
    AI agent frameworks and workflow orchestration
   </strong>
   .
  </li>
  <li>
   Experience with
   <strong>
    APIs, system integration, SQL and data pipelines
   </strong>
   .
  </li>
  <li>
   Exposure to AI evaluation and testing frameworks.
  </li>
  <li>
   Experience with cloud platforms and deploying/managing applications in cloud environments.
  </li>
  <li>
   Exposure to lightweight front-end development for internal tools is an advantage.
  </li>
  <li>
   Strong process mapping, business analysis and requirements-gathering skills.
  </li>
  <li>
   Ability to communicate technical concepts to non-technical stakeholders.
  </li>
  <li>
   Experience working directly with clients, business units or operational teams is strongly preferred.
  </li>
  <li>
   Exposure to
   <strong>
    logistics, supply chain or operations-heavy environments
   </strong>
   is an advantage.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  Key Technical Skills
 </p>
 <p>
  <strong>
   Python | Generative AI | LLMs | LLM APIs | Prompt Engineering | RAG | Vector Databases | AI Agents | Workflow Orchestration | API Integration | SQL | Data Pipelines | AI Evaluation | Cloud Deployment | Software Development | Process Automation
  </strong>
 </p>
 <p>
  What We Are Looking For
 </p>
 <p>
  We are looking for an engineer who is comfortable operating at the intersection of
  <strong>
   technology and business
  </strong>
  .
 </p>
 <p>
  You should be able to take an ambiguous operational problem, understand it directly from users, design a technical solution, build it, deploy it into production and ensure that the business can successfully use and maintain it.
 </p>
 <p>
  <strong>
   Candidates whose experience is limited to academic projects, training or POCs without production deployment experience will not be considered.
  </strong>
 </p>
</div>


Forward Deployed Engineer

Halian | Managed Services, Recruitment Agency & Contract Staffing

<div>
 <p>
  Halian is hiring Forward Deployed AI Engineer
  <strong>
  </strong>
  in Abu Dhabi.
 </p>
 <p>
  <br/>
 </p>
 <p>
  Greetings!
 </p>
 <p>
  <br/>
 </p>
 <p>
  Experience required: 8+ Years
 </p>
 <p>
  Duration: 1 year &amp; extendable
 </p>
 <p>
  <br/>
 </p>
 <p>
  <br/>
 </p>
 <p>
  Technical Skills:
 </p>
 <ul>
  <li>
   8+ years of experience in building and delivering production-grade software solutions.
  </li>
  <li>
   4+ years of hands-on experience with Generative AI, Large Language Models (LLMs), or applied Machine Learning.
  </li>
  <li>
   Minimum 1 year of experience designing and deploying agentic AI solutions.
  </li>
  <li>
   Proven track record of setting technical direction and delivering AI solutions at scale.
  </li>
  <li>
   Hands-on experience or strong working knowledge of Model Context Protocol (MCP) for connecting AI agents with systems, tools, APIs, and data sources.
  </li>
  <li>
   Strong proficiency in Python programming.
  </li>
  <li>
   Hands-on expertise in at least one of the following languages: TypeScript/JavaScript, Java, or C#.
  </li>
  <li>
   Strong understanding of modern engineering practices, including asynchronous programming, FastAPI, Pydantic, CI/CD, testing strategies, source control, logging, and error handling.
  </li>
  <li>
   Hands-on experience with at least one leading agent framework or enterprise AI platform, such as LangGraph, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, Microsoft Foundry, Amazon Bedrock AgentCore, or Google Vertex AI/Gemini, for building and deploying scalable AI applications.
  </li>
  <li>
   Hands-on experience with vector databases and enterprise search platforms, including Azure AI Search, pgvector, Pinecone, Weaviate, or OpenSearch.
  </li>
  <li>
   Experience integrating enterprise systems through APIs, managed identities, middleware, webhooks, messaging queues, and cloud-native architectures.
  </li>
  <li>
   Practical experience deploying solutions in cloud environments using containers, monitoring, and observability platforms.
  </li>
  <li>
   Strong judgment regarding trade-offs between quality, latency, reliability, security, governance, and cost.
  </li>
  <li>
   Demonstrated curiosity and a passion for understanding complex business and operational challenges.
  </li>
  <li>
   Strong business-first, human-centered approach, recognizing that AI augments people rather than replacing them.
  </li>
  <li>
   Excellent communication skills in English and experience working within diverse, international teams.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Halian Group
  </strong>
  : With over 28 years of experience, we have come to understand that innovation is the only way to provide agile, practical solutions that transform businesses and careers. Our resourcing and smart services help you to realize tomorrow's potential. Discover the amazing things possible when you bring the right people and the right technologies together.
 </p>
</div>


Forward Deployed AI Engineer

edari

<div>
 <p>
  We are currently seeking a AI Engineer who will be responsible for designing, building, integrating, and operationalizing enterprise-grade AI solutions across the organization. The role will focus on practical AI engineering using Microsoft Copilot, Copilot Studio with the underlying models from OpenAI, Anthropic, and modern Generative AI capabilities to improve business processes, knowledge access, productivity, and digital service delivery.
 </p>
 <p>
  <br/>
 </p>
 <p>
  You will work closely with business stakeholders, IT delivery teams, data owners, and vendors to translate business needs into secure, scalable, and maintainable AI-enabled solutions. The ideal candidate combines strong technical AI development experience with sound business understanding, clear communication skills, and the ability to deliver AI solutions responsibly within an enterprise environment.
 </p>
 <p>
  <br/>
 </p>
 <p>
  Our client based in
  <strong>
   Dubai
  </strong>
  , is one of the largest semi-governmental conglomerates in the region. This position is a
  <strong>
   contract
  </strong>
  role with an initial duration o
  <strong>
   f 12 months
  </strong>
  and is
  <strong>
   renewab
  </strong>
  l
  <strong>
   e.
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Key Responsibilities:
  </strong>
 </p>
 <ul>
  <li>
   Design, develop, test, and deploy AI-enabled solutions using Copilot, Copilot Studio, LLM APIs, orchestration frameworks, and enterprise integration patterns.
  </li>
  <li>
   Build practical GenAI use cases such as conversational assistants, knowledge retrieval experiences, workflow copilots, document intelligence, summarization, and productivity automation.
  </li>
  <li>
   Develop prototypes and production-ready AI components that integrate with enterprise systems, data sources, APIs, and Microsoft 365 services.
  </li>
  <li>
   Configure and extend Microsoft 365 Copilot, Copilot Studio agents, custom copilots, topics, actions, connectors, prompts, and enterprise knowledge grounding patterns.
  </li>
  <li>
   Work with OpenAI / ChatGPT and Claude models, including effective prompt design, model selection, response evaluation, guardrails, context handling, and performance optimization.
  </li>
  <li>
   Apply Retrieval-Augmented Generation (RAG), embeddings, vector search, function calling, tool use, and agentic workflow concepts where appropriate.
  </li>
  <li>
   Collaborate Techno-Functional developers to define AI solution designs, integration approaches, APIs, data flows, authentication, and security boundaries.
  </li>
  <li>
   Connect AI solutions to structured and unstructured enterprise data sources while considering data quality, permissions, sensitivity, and governance requirements.
  </li>
  <li>
   Produce technical documentation including architecture notes, integration specifications, prompt libraries, model evaluation notes, and operational runbooks.
  </li>
  <li>
   Embed responsible AI principles, privacy, compliance, security, and risk considerations into AI solution design and delivery from the start.
  </li>
  <li>
   Support model and prompt evaluation, hallucination control, access control validation, data leakage prevention, and safe-use guidance for business users.
  </li>
  <li>
   Contribute to AI governance artefacts, standards, reusable patterns, and technical controls for enterprise AI adoption.
  </li>
  <li>
   Work with business stakeholders to understand pain points, assess AI feasibility, refine requirements, and translate business needs into technical AI solutions.
  </li>
  <li>
   Support discovery workshops, solution demos, user testing, adoption activities, and continuous improvement cycles for AI-enabled products and services.
  </li>
  <li>
   Balance technical feasibility, business value, user experience, delivery effort, and operational sustainability when shaping AI use cases.
  </li>
  <li>
   Apply software engineering practices including version control, code review, testing, environment management, deployment discipline, and ongoing monitoring.
  </li>
  <li>
   Evaluate AI outputs using defined quality criteria, feedback loops, test datasets, prompt regression checks, and business acceptance measures.
  </li>
  <li>
   Stay current with developments across Copilot, OpenAI, Claude, Azure AI, GenAI frameworks, and emerging enterprise AI patterns.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Knowledge, Skills &amp; Experience:
  </strong>
 </p>
 <ul>
  <li>
   5+ years of technology delivery or software engineering experience, with meaningful hands-on exposure to AI, automation, data, or enterprise application development.
  </li>
  <li>
   Hands-on experience with Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry / Azure Open AI, or comparable enterprise AI platforms.
  </li>
  <li>
   Practical experience designing or developing AI-enabled solutions using LLMs, APIs, prompt engineering, workflow automation, or intelligent assistants.
  </li>
  <li>
   Experience with OpenAI / ChatGPT, Claude, embeddings, vector databases, RAG architectures, LangChain / Semantic Kernel, or agentic AI frameworks.
  </li>
  <li>
   Strong understanding of Generative AI concepts, model behaviour, prompting techniques, grounding, evaluation, limitations, and responsible AI considerations.
  </li>
  <li>
   Experience integrating AI solutions with Microsoft 365, SharePoint, Teams, Power Platform, Dynamics 365, Salesforce, ERP, ITSM, or other enterprise systems.
  </li>
  <li>
   Ability to translate business requirements into technical designs, prototypes, production-ready AI features, and measurable business outcomes.
  </li>
  <li>
   Knowledge of data governance, privacy, security, access control, content classification, and enterprise compliance requirements.
  </li>
  <li>
   Working knowledge of APIs, JSON, authentication concepts, secure integration patterns, data handling, and software development lifecycle practices.
  </li>
  <li>
   Experience in real estate, leasing, asset management, customer experience, or other business domains relevant to enterprise operations.
  </li>
  <li>
   Strong analytical, troubleshooting, communication, and documentation skills, with the ability to explain technical AI concepts to non-technical stakeholders.
  </li>
  <li>
   Cloud, AI, Agile, or software engineering certifications such as Microsoft AI / Azure certifications, Scrum, SAFe, or equivalent credentials.
  </li>
  <li>
   Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Technology, or a related field.
  </li>
  <li>
   Consulting, product delivery, or client-facing experience delivering AI, automation, or digital transformation solutions.
  </li>
 </ul>
</div>


Artificial Intelligence Engineer

<div>Position Overview:
<div>
This is a forward / full-stack engineer who is AI-enabled rather than an AI specialist. The person builds pipelines, full stack features, AI service contracts &mdash; and bring AI surfaces to life inside DE product UIs and APIs, using AI tooling to accelerate their own delivery.
</div>Responsibilities:
<ul><li>Build and operate the data pipelines, feature that power DE AI use cases, aligned to SBS Data &amp; AI Platform standards.</li><li>
Integrate SBS AI Platform services (Gen, Agentic, Predictive) into DBS and OB through clean APIs, SDKs and event contracts, and wire them into product screens and flows.</li><li>
Implement governance by design: RBAC, data classification, lineage, PII handling, audit logs and EU AI Act traceability.</li><li>
Use AI-assisted tooling (copilots, MCP, agents) to accelerate delivery and extensibility, and support the QA Engineer with test data and observability.</li></ul>
Qualifications:
<ul><li>4+ years in data / backend or full-stack engineering, comfortable working across the stack.</li><li>
Strong Python and SQL; streaming (Kafka), data lakes/warehouses, vector stores and orchestration (Airflow / Dagster).</li><li>
Practical fluency with AI-enabled engineering &mdash; LLM serving, model gateways, prompt engineering, agentic frameworks and AI coding tools.</li><li>
Banking data context (accounts, transactions, KYC, OB consent) preferred.</li></ul>
Company Overview:<div></div><div><div><br>
At SBS, we&rsquo;re more than just a technology company &ndash; we&rsquo;re a global fintech partner helping banks and financial institutions transform, innovate, and grow. With over 5,000 employees worldwide and clients in more than 80 countries, our solutions power everything from digital banking and lending to payments and core banking systems.</div><div></div><div><br>
As part of 74Software, we&rsquo;re backed by a group of leading software companies delivering mission-critical solutions worldwide. Our focus is on delivering long-term value, leveraging cutting-edge technology, and fostering strong client partnerships. Join us and be part of a collaborative, forward-thinking team shaping the future of finance.</div><div></div><div><b><br>
Equal Opportunity Employer</b></div><div>
At our organization, we are committed to fighting against all forms of discrimination. We foster a work environment that is inclusive and respectful of all differences.</div><div>
All of our positions are open to people with disabilities.</div></div></div><div></div>

Software Engineer III

Fuse Energy

<div>
 Fuse Energy is an energy startup on a mission to make energy abundant and affordable, fast. We combine first-principles thinking with cutting-edge technology to build a radically better energy system.
 <br/>
 <br/>
 We've raised over $200M from top-tier investors including Balderton, Lakestar, Accel, Creandum, Lowercarbon, Ribbit, 20VC, Hummingbird and Collaborative Fund, alongside strategic angels including Nico Rosberg and GPs behind Meta, Revolut, Spotify and Uber.
 <br/>
 <br/>
 We're building a fully integrated energy company: developing our own solar, batteries and other generation projects, building our own hardware, improving and developing grid infrastructure, trading power in real time, using AI across the business, and installing distributed energy in homes. By selling directly to consumers we cut out the middleman, lower costs and pass the savings on to our customers.
 <br/>
 <br/>
 We're building a cutting-edge AI team. As an Applied AI Engineer, this role suits someone with the technical depth of a backend engineer who is specifically interested in applied AI and how it can improve the energy experience for our customers and our internal operations. You'll work on consumer features such as the Energy Co-Pilot and speedy onboarding (using VLM and LLM tools) and build AI tools that make teams across Fuse more productive.
 <br/>
 <br/>
 <strong>
  Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Design, develop and deploy AI-powered features that directly impact consumer experiences, including personalised energy recommendations and seamless onboarding via AI models (e.g. using energy bills for quick setup)
  </li>
  <li>
   Build and optimise internal AI tools that make the whole company more productive, with a focus on automation and enhancing workflows
  </li>
  <li>
   Collaborate with backend engineers and data scientists to integrate AI-driven features into our platforms
  </li>
  <li>
   Collaborate with the trading and operations teams to ensure AI models are aligned with real-time market conditions and energy pricing
  </li>
  <li>
   Improve AI models to optimise trading strategies by anticipating market shifts based on weather and demand forecasts
  </li>
  <li>
   Stay up to date with the latest advancements in applied AI and machine learning and apply them to real-world problems in the energy space
  </li>
  <li>
   Monitor the performance of AI tools and models, ensuring they run efficiently and effectively
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Requirements
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Minimum 3 years of engineering experience
  </li>
  <li>
   Proven experience as a backend engineer with a strong interest and practical experience in applied AI or machine learning
  </li>
  <li>
   Strong programming skills in Python (or similar) with familiarity in AI/ML libraries (TensorFlow, PyTorch, etc.)
  </li>
  <li>
   Experience working with large-scale models (LLMs/VLMs) and deploying AI-driven solutions into production
  </li>
  <li>
   Solid understanding of cloud technologies, containerisation and building scalable AI applications
  </li>
  <li>
   Ability to integrate AI/ML models into real-world applications, focusing on usability and performance
  </li>
  <li>
   Strong problem-solving skills and a practical approach to implementing AI solutions in a fast-paced environment
  </li>
  <li>
   Experience working with large datasets, particularly in relation to demand and supply forecasting
  </li>
  <li>
   Bonus: experience or strong interest in energy markets and trading strategies; understanding of weather forecasting, energy demand patterns and production modelling; exposure to NLP or related fields
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Benefits
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Competitive salary and eligibility for equity
  </li>
  <li>
   Biannual bonus scheme
  </li>
  <li>
   Fully expensed tech to match your needs
  </li>
  <li>
   Private health insurance
  </li>
  <li>
   Breakfast and dinner allowance for office-based employees
   <br/>
   <br/>
  </li>
 </ul>
 As we hire globally, benefits may vary by location.
</div>


Applied AI Engineer

Capgemini

<div><div><div><div><p>At Capgemini Invent, we believe difference drives change. As inventive transformation consultants, we blend our strategic, creative and scientific capabilities, collaborating closely with clients to deliver cutting-edge solutions. Join us to drive transformation tailored to our client's challenges of today and tomorrow. Informed and validated by science and data. Superpowered by creativity and design. All underpinned by technology created with purpose.</p>
<div><div><div><h2 class="jobSectionHeader"><b>Your Role</b></h2>
</div><div><ul><li>Lead architecture and technical design for enterprise Azure AI solutions across GenAI, agentic AI, RAG, AI Search, integration, security, observability and AI Factory platform patterns.</li>
<li>Design Azure AI architectures for GenAI, agentic AI, RAG, enterprise copilots and AI Factory platforms.</li>
<li>Define patterns across Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure ML, Fabric, Databricks and integration services.</li>
<li>Lead technical discovery with IT, data, security and enterprise architecture stakeholders.</li>
<li>Create reference architectures, proposal solution blueprints, technical assumptions and PoC plans.</li>
<li>Guide MVPs covering environment setup, secure connectivity, data ingestion, retrieval, agents, evaluation and monitoring.</li>
<li>Embed guardrails, content safety, logging, identity, access control and human oversight into designs.</li>
</ul></div></div><div><div><h2 class="jobSectionHeader"><b>Experience and Skills Required</b></h2>
</div><div><ul><li>8-12+ years in solution architecture, cloud architecture, data/AI architecture or platform engineering.</li>
<li>4-5+ years designing Azure solutions, ideally with production AI or client-facing implementations.</li>
<li>Hands-on GenAI, RAG, LLM app, agentic AI, MLOps/LLMOps or AI platform engineering exposure.</li>
<li>Secure enterprise integration experience: identity, APIs, private networking, monitoring and cloud governance.Strong experience in <b>Python</b> is a plus but it&rsquo;s knowledge is mandatory.</li>
<li>Azure AI Foundry and Azure OpenAI.</li>
<li>Azure AI Search, vector search, embeddings and RAG.</li>
<li>Azure Machine Learning, evaluation and deployment concepts.</li>
<li>Microsoft Fabric, Azure Databricks, ADLS Gen2 and data integration.</li>
<li>Azure Functions, Logic Apps, Event Grid, Service Bus and API Management.</li>
<li>Entra ID, managed identities, Key Vault, private endpoints, VNet integration, RBAC and logging.</li>
<li>Agentic AI, tool calling, orchestration, memory, observability and evaluation.</li>
<li>Preferred certifications: Azure Solutions Architect Expert and Azure AI Engineer Associate.</li>
<li>Experience in AI and Cloud FinOps</li><br>
</ul></div></div></div><p>Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.</p></div></div></div></div>

Senior Azure AI Architect

AGMC

<div>
 <p>
  <strong>
   About the Role
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  We're looking for an
  <strong>
   AI Engineer
  </strong>
  who can take ideas from prototype to production &mdash; building scalable AI/ML systems and the data architecture behind them. This is a hands-on role combining strong
  <strong>
   software engineering, applied machine learning, Generative AI and data engineering
  </strong>
  .
 </p>
 <p>
  The ideal candidate will be responsible for developing practical AI solutions that support business transformation, automation and improved decision-making across AGMC.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   What You'll Do
  </strong>
 </p>
 <ul>
  <li>
   Design, build and optimize
   <strong>
    ML/DL models and LLM-powered applications
   </strong>
   , including Agentic AI, Generative AI and RAG solutions.
  </li>
  <li>
   Build end-to-end AI pipelines covering
   <strong>
    training, inference, evaluation and monitoring
   </strong>
   .
  </li>
  <li>
   Develop clean, scalable and well-tested backend services and APIs using
   <strong>
    FastAPI / Flask
   </strong>
   .
  </li>
  <li>
   Design data architecture and manage data storage, primarily using
   <strong>
    PostgreSQL
   </strong>
   , including schema design, data modelling and performance optimization.
  </li>
  <li>
   Work with large datasets, including data preprocessing, feature engineering and model evaluation.
  </li>
  <li>
   Develop and integrate AI solutions into existing business applications and systems.
  </li>
  <li>
   Monitor AI/ML models in production and continuously improve
   <strong>
    accuracy, latency, reliability and scalability
   </strong>
   .
  </li>
  <li>
   Develop proof-of-concepts and take successful AI solutions through to production.
  </li>
  <li>
   Collaborate with
   <strong>
    IT, Digital, Product, Data and business teams
   </strong>
   to identify and deliver AI use cases.
  </li>
  <li>
   Ensure AI solutions follow appropriate
   <strong>
    security, privacy, governance and responsible AI practices
   </strong>
   .
  </li>
  <li>
   Stay up to date with emerging AI technologies and identify opportunities to apply them within AGMC.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   What We're Looking For
  </strong>
 </p>
 <ul>
  <li>
   Strong
   <strong>
    Python
   </strong>
   skills and solid software engineering fundamentals, including clean code, testing and Git.
  </li>
  <li>
   Hands-on experience with
   <strong>
    PyTorch and/or TensorFlow
   </strong>
   .
  </li>
  <li>
   Practical experience with
   <strong>
    Agentic AI, Generative AI, LLMs and RAG
   </strong>
   .
  </li>
  <li>
   Experience with LLM frameworks and tooling such as
   <strong>
    LangChain and/or LlamaIndex
   </strong>
   .
  </li>
  <li>
   Strong data architecture and database skills, particularly
   <strong>
    PostgreSQL
   </strong>
   .
  </li>
  <li>
   Familiarity with
   <strong>
    vector databases
   </strong>
   such as Pinecone, Weaviate or FAISS.
  </li>
  <li>
   Experience with cloud platforms such as
   <strong>
    AWS, Microsoft Azure or Google Cloud Platform
   </strong>
   .
  </li>
  <li>
   Understanding of
   <strong>
    MLOps
   </strong>
   and production deployment, including Docker, Kubernetes, CI/CD and MLflow.
  </li>
  <li>
   Experience developing and consuming
   <strong>
    REST APIs
   </strong>
   .
  </li>
  <li>
   Strong analytical and problem-solving skills.
  </li>
  <li>
   Clear communicator with the ability to work independently and take ownership of projects from concept through to production.
  </li>
  <li>
   Experience working in an
   <strong>
    Agile / collaborative environment
   </strong>
   .
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Nice to Have
  </strong>
 </p>
 <ul>
  <li>
   Bachelor's or Master's degree in
   <strong>
    Computer Science, Artificial Intelligence, Data Science, Software Engineering or a related field
   </strong>
   .
  </li>
  <li>
   Maximum 2 years experience deploying AI applications at scale within an enterprise environment.
  </li>
  <li>
   Strong
   <strong>
    prompt engineering
   </strong>
   skills and understanding of AI safety and responsible AI.
  </li>
  <li>
   Experience with AI agents and multi-agent architectures.
  </li>
  <li>
   Exposure to
   <strong>
    reinforcement learning or multimodal AI
   </strong>
   .
  </li>
  <li>
   Experience working with
   <strong>
    automotive, retail, mobility or other customer-focused industries
   </strong>
   would be an advantage.
  </li>
  <li>
   Experience taking AI solutions from
   <strong>
    proof-of-concept to production
   </strong>
   .
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Tools &amp; Technologies
  </strong>
 </p>
 <p>
  <strong>
   Python &middot; FastAPI / Flask &middot; PyTorch / TensorFlow &middot; PostgreSQL &middot; SQL &middot; LangChain / LlamaIndex &middot; RAG &middot; Vector Databases &middot; Docker &middot; Kubernetes &middot; Git &middot; AWS &middot; Azure &middot; GCP &middot; MLflow &middot; CI/CD
  </strong>
 </p>
</div>


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