{"id":4087508,"url":"https://alion.io/job/sanlam-ai-solutions-engineer-associate","title":"AI Solutions Engineer (Associate)","company":{"id":2194082,"name":"Sanlam","domain":"sanlam.com","url":"https://alion.io/company/sanlam-co-za","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":{"grade":"A","score":95,"open_postings":5,"ghost_share":0,"stale_share":0.4,"repost_share":0,"time_to_fill_p50_days":3,"computed_at":"2026-11-11T05:49:22Z"}},"role":"Solutions","role_family":"Solutions","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["South Africa"],"countries":["ZA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"Machine Learning","optional":false},{"name":"RAG","optional":false},{"name":"Agentic Workflows","optional":true},{"name":"AWS","optional":true},{"name":"AWS Bedrock AgentCore","optional":true},{"name":"AWS Strands Agents","optional":true},{"name":"Azure","optional":true},{"name":"Azure DevOps","optional":true},{"name":"CI/CD","optional":true},{"name":"CrewAI","optional":true},{"name":"Docker","optional":true},{"name":"Git","optional":true},{"name":"GitHub Actions","optional":true},{"name":"GitLab CI","optional":true},{"name":"Hallucination","optional":true},{"name":"Java","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"Rest API","optional":true},{"name":"Scikit-learn","optional":true},{"name":"SQL","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-11-11T19:18:07Z","employer_posted_date":"2026-11-11","last_verified_at":"2026-11-11T19:18:07Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Who are we?\nSanlam Life and Savings is one of the key businesses within Sanlam, focused on helping clients build, protect and preserve their financial futures. Through our technology, products and people, we support millions of client and intermediary interactions across South Africa.\nBusiness Transformation and Shared Services Technology, or BTSS Tech, owns and enables important technology platforms that allow existing clients to engage with Sanlam through servicing, claims, complaints, collections and payments. We also provide the technology that enables Sanlam’s Customer Service teams to support clients effectively.\nPurpose of the Role\nThe Opportunity\nBTSS Tech has an ambitious mandate: run today’s critical technology with excellence, continuously optimise it, and modernise the technology landscape that will power Sanlam’s future customer experience.\nAs an AI Solutions Engineer, you will help us identify, build and operationalise practical AI solutions that improve customer journeys, support service employees, simplify processes and strengthen technology operations.\nThis is a hands-on junior to intermediate engineering role for someone who has established foundations in software, data or AI engineering and is ready to apply them to real business problems. \nYou will work alongside experienced engineers, architects, product teams and business stakeholders, learning rapidly while taking increasing ownership for solutions from experimentation through to production.\nYou will not simply experiment with AI. You will help turn promising ideas into responsible, secure and useful solutions that work in the real world.\nJob Purpose:\nThe AI Solutions Engineer turns emerging AI capabilities into secure, scalable and production-ready solutions that improve customer experiences, empower service teams and help modernise the BTSS Technology landscape. The role works across Generative AI, intelligent automation, data and software engineering.\nYou will contribute hands-on across the solution lifecycle while building your capability in RAG, agentic AI, cloud engineering and modern software development practices.\nWhat will you do\n1. Build AI-enabled solutions\nDesign and develop components for machine learning, generative AI and intelligent automation solutions.\nBuild prototypes and proofs of concept that test whether an AI opportunity is technically feasible, desirable for users and valuable to the business.\nDevelop AI-enabled applications such as:o knowledge assistants;\no service-agent copilots;\no intelligent search solutions;\no document classification and information extraction;\no summarisation and response-support tools;\no customer-service workflow automation;\no technology operations and engineering assistants\n\nAssist in building agentic workflows that can reason across multiple steps, use approved tools and APIs, and complete controlled actions with appropriate human oversight.\n.2. Engineer solutions for production\nWrite clean, modular, testable and maintainable code.\nPackage AI capabilities into APIs, services, applications or reusable components that can integrate with BTSS platforms.\nCollaborate with software engineers and architects to connect AI solutions to customer engagement, CRM, service, payments and other enterprise platforms.\nUse version control, code reviews, automated testing and CI/CD practices to improve engineering quality.\nContainerise and deploy solution components using approved cloud and platform services.\nContribute to the monitoring, support and continuous improvement of AI solutions once they are in production.\n3. Work with data and enterprise knowledge\nPrepare, transform and validate structured and unstructured data for use in AI solutions.\nDevelop data ingestion and processing components using Java, SQL and relevant data-engineering tools.\nConnect AI solutions safely to approved internal data, documents, APIs and knowledge repositories.\nHelp establish traceability between source information and AI-generated outputs.\nApply data-quality, privacy, security and access-control requirements throughout the solution lifecycle.\nWork with data engineers and platform teams to ensure that solutions use reliable, governed and appropriately authorised data.\n4. Evaluate AI quality and risk\nDevelop test cases and evaluation datasets for AI solutions.\nEvaluate outputs for relevance, accuracy, groundedness, consistency and usefulness.\nHelp identify and reduce risks such as hallucination, bias, prompt injection, inappropriate data exposure and unreliable automated actions.\nMeasure solution performance using agreed technical, operational and user-experience metrics.\nImplement logging, monitoring, feedback and human-review mechanisms.\nDocument known limitations and ensure that AI capabilities are used within clearly defined boundaries.\n5. Solve customer and operational problems\nWork with product owners, service teams and subject-matter experts to understand customer and employee pain points.\nTranslate business needs into AI use cases, technical requirements and testable hypotheses.\nParticipate in discovery sessions, technical design discussions and solution demonstrations.\nExplain technical choices, limitations and trade-offs clearly to both technical and non-technical stakeholders.\nUse feedback and evidence to improve solutions through short, iterative delivery cycles.\n6. Contribute to a modern engineering culture\nParticipate actively in code reviews, technical showcases, design discussions and team retrospectives.\nDocument reusable patterns, technical decisions and lessons learned.\nShare knowledge with colleagues and contribute to BTSS’s growing AI engineering capability.\nExperiment responsibly with emerging technologies and assess their relevance to BTSS.\nLearn from experienced engineers while progressively taking ownership for more complex solution components.\nWhat success will look like\nWithin your first 12 months, you will be expected to:\ncontribute meaningfully to at least one AI solution that progresses beyond a proof of concept;\nindependently develop and test defined solution components;\ndemonstrate sound engineering practices in code quality, testing, documentation and version control;\nbuild a practical understanding of BTSS’s customer, service and technology environments;\napply responsible AI, security and data-governance requirements in your work;\nwork effectively across software, data, product and business teams;\nshow clear growth in your ability to take a problem from discovery through to implementation.\nQualifications\nA relevant bachelor’s degree, advanced diploma or equivalent practical foundation in one or more of the following areas:\nComputer Science;\nSoftware Engineering;\nData Science;\nInformation Systems;\nElectronic or Computer Engineering;\nMathematics or Statistics;\nanother relevant technology or quantitative discipline.\nRelevant postgraduate study, industry certifications, technical boot camps, open-source contributions or a strong practical project portfolio may be considered as additional evidence of capability.\nExperience\nThis role is suited to an early-career or developing engineer who typically has:\napproximately 2 to 4 years of relevant experience in software engineering, data engineering, machine learning engineering, cloud engineering or AI solution development;\npractical experience developing solutions in Python;\nexposure to taking software, data or AI components beyond a notebook or isolated prototype;\nexperience working in a collaborative development environment using Git;\nevidence of practical AI, ML or GenAI work through employment, internships, academic projects, personal projects or open-source contributions.\nCandidates are not expected to be experts in every listed technology. We are looking for strong foundations, evidence of practical building, sound engineering judgement and the capacity to learn quickly.\nTechnical capability\nEssential foundations\nJava programming, including readable code, modular design and fundamental object-oriented concepts.\nSQL and a working understanding of relational databases.\nREST APIs, JSON and common system-integration patterns.\nGit and standard version-control workflows.\nSoftware testing, debugging and technical documentation.\nFoundational understanding of cloud services and cloud-based application deployment.\nFundamental understanding of machine learning and generative AI concepts.\nAI and machine learning exposure\nLarge language models and prompt design.\nAI orchestration frameworks such as LangChain, LangGraph, AgentCore, Strands Agents, CrewAI or comparable technologies.\nFrameworks such as Scikit-learn, PyTorch or TensorFlow.\nEvaluation and monitoring of AI-generated outputs.\nCloud and engineering exposure\nExperience or practical exposure in some of the following will be advantageous:\nAWS, Microsoft Azure or another leading cloud platform;\nDocker and containerised deployment;\nCI/CD pipelines, including GitHub Actions, GitLab CI, Azure DevOps or equivalent;\nmanaged AI and machine learning services;\nserverless services and event-driven architectures;\nInfrastructure as Code;\nobservability, logging and application monitoring;\nsecure application development and DevSecOps practices.\nPersonal qualities\nWe are looking for someone who demonstrates:\nCuriosity: You actively explore how new technologies work and where they can create meaningful value.\nBuilder mindset: You enjoy turning ideas into working solutions, not simply discussing possibilities.\nCustomer orientation: You remain focused on the customer or employee problem that the technology must solve.\nEngineering discipline: You care about quality, security, maintainability and reliability.\nLearning agility: You can learn unfamiliar tools and concepts quickly.\nPersistence: You troubleshoot methodically and continue iterating when the first approach does not work.\nCollaboration: You work constructively with people from different technical and business disciplines.\nClear communication: You can explain your work, assumptions and trade-offs without unnecessary technical complexity.\nResponsible judgement: You understand that enterprise AI must be implemented with appropriate controls, transparency and human accountability.\nWhy join SLS BTSS Tech?\nBTSS Tech sits at the intersection of technology transformation and customer experience. The solutions we build influence how clients engage with Sanlam and how effectively our service teams can support them.\nYou will have the opportunity to:\napply AI to meaningful customer and operational challenges;\nwork across established enterprise platforms and emerging technologies;\nhelp modernise a complex and important technology landscape;\ndevelop practical experience across AI, software, cloud, data and integration engineering;\nlearn from experienced colleagues while bringing new thinking and energy into the team;\ncontribute directly to Sanlam’s Customer North Star and Vision 2030 ambitions.\nYou will not simply inherit technology. You will help shape what it becomes.\nTurnaround time\nThe shortlisting process will only start once the application due date has been reached. The time taken to complete this process will depend on how far you progress and the availability of managers.\nClosing Date: 25 November 2026\nThe Sanlam Group is committed to achieving transformation and embraces diversity. This commitment is what drives us to achieve a diverse, inclusive and equitable workplace as we believe that these are key components to ensuring a thriving and sustainable business in South Africa. 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