{"id":4019958,"url":"https://alion.io/job/adams-adams-ai-engineer","title":"AI Engineer","company":{"id":1894671,"name":"Adams & Adams","domain":"adams.africa","url":"https://alion.io/company/adams-africa","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"MCI DirectHire","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pretoria, South Africa"],"countries":["ZA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":".NET","optional":false},{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"C#","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Copilot","optional":false},{"name":"Copilot Studio","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"JavaScript","optional":false},{"name":"Least Privilege","optional":false},{"name":"LLMOps","optional":false},{"name":"OpenAI","optional":false},{"name":"Power Automate","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"SQL","optional":false},{"name":"TypeScript","optional":false},{"name":"Agentic Workflows","optional":true},{"name":"Azure DevOps","optional":true},{"name":"Context Engineering","optional":true},{"name":"Embeddings","optional":true}],"status":"live","first_seen_at":"2026-11-10T12:00:00Z","employer_posted_date":"2026-11-10","last_verified_at":"2026-11-10T19:40:47Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Description\nPurpose of the Role\nThe AI Engineer is responsible for designing, building, testing, deploying and supporting secure AI-enabled applications, intelligent agents, workflow automations and enterprise integrations. The role translates approved business requirements and future-state processes into maintainable and measurable solutions that improve service delivery, productivity, operational control and client experience.\nThe AI Engineer works closely with the Business Analyst, Process Engineer, Software Engineering, Enterprise Solutions, Data & AI, Information Security, Project Management, business stakeholders and external delivery partners.\nThe role applies a governed and human-controlled engineering approach. AI-assisted outputs must be reviewed, tested and approved before deployment. Legal, statutory, financial and compliance-related decisions must remain deterministic, auditable or subject to appropriate human approval.\nDuties and ResponsibilitiesStrategy & Planning\nReview approved business requirements, process maps, business rules, data definitions, controls and acceptance criteria with the Business Analyst and Process Engineer.\nIdentify unclear requirements, conflicting rules, data gaps, security considerations and non-functional requirements before development begins.\nTranslate approved requirements into engineering-ready work items with clearly defined inputs, outputs, constraints and acceptance tests.\nDetermine whether a requirement is best addressed through deterministic software, workflow automation, agentic AI, retrieval, low-code configuration or a combination of solutions.\nAssess and recommend appropriate AI tools, platforms and models based on architecture, security, licensing, cost and business requirements.\nDefine measurable success criteria relating to accuracy, reliability, workflow completion, security, operational performance and business value.\nDevelopment & Deployment\nDesign and develop AI-enabled applications, agents, agent flows, APIs, integrations, validation services, data components and user interfaces.\nBuild governed enterprise agents and workflows using Microsoft Copilot Studio, Power Automate, Azure AI services and approved Microsoft 365 technologies.\nUse approved AI-assisted development tools, including Claude Code and GitHub Copilot, to support software development, testing, documentation and refactoring.\nDevelop retrieval-augmented generation and document-intelligence solutions using approved, authoritative and access-controlled information sources.\nBuild controlled integrations with platforms such as Patricia, Aderant, NetDocuments, Sage, October People and Microsoft 365.\nImplement appropriate human approval, access control, exception handling, audit trails and escalation mechanisms within AI-enabled solutions.\nEnsure AI-generated software artefacts pass human review, automated testing, security checks and approved repository and deployment controls.\nMaintain appropriate versioning, testing, deployment, configuration and rollback processes for code, prompts, agents, models and connectors.\nOperational Management\nDevelop and maintain evaluation datasets and test suites covering accuracy, grounding, workflow completion, tool use, security boundaries and unacceptable behaviour.\nConduct unit, integration, regression and end-to-end testing across normal, ambiguous, adversarial and failure scenarios.\nMonitor solution availability, latency, error rates, tool calls, workflow outcomes, human overrides and AI consumption costs.\nInvestigate incidents, failed workflows and degraded outputs, conduct root-cause analysis and implement corrective controls.\nApply information security, confidentiality, legal privilege, POPIA, records-management and responsible AI requirements throughout the solution lifecycle.\nImplement role-based access, least-privilege permissions, approval gates, audit logs, content safeguards and emergency disablement controls.\nMaintain current technical documentation, operational runbooks, architecture records, deployment information and escalation procedures.\nMeasure delivered solutions against agreed business outcomes, including turnaround time, manual effort, rework, errors, service quality and control performance.\nDemonstrate working solutions, incorporate stakeholder feedback and support user training, adoption and knowledge transfer.\nDevelop reusable prompts, connectors, evaluation assets, coding standards and engineering patterns to improve consistency and reduce future delivery effort.\nKey improvements\nThe original responsibilities have been grouped into Strategy & Planning, Development & Deployment, and Operational Management, matching the structure of your Business Analyst example. The wording has also been condensed without removing the role’s core focus on agentic AI, enterprise integration, responsible AI, testing, security and operational reliability.\nRequirements\nQualificationsEducation\nRelevant Bachelor’s degree or diploma in Computer Science, Software Engineering, Data Science, Information Systems, Artificial Intelligence or a related discipline.\nRelevant certifications in Microsoft Azure AI, Power Platform, Copilot Studio, software development, cloud, data or security would be advantageous.\nExperience\nTypically 4-7+ years’ experience in software engineering, integration, automation, data engineering or AI solution delivery, including practical Generative AI or Agentic AI experience.\nDemonstrated experience building and supporting production software, APIs, integrations, automated testing and controlled deployments.\nPractical experience designing and implementing AI agents, workflow orchestration, tool use, human approvals, grounding, evaluation and monitoring.\nExperience using AI-assisted development tools within a controlled software development and DevOps environment.\nExperience integrating cloud and enterprise platforms through APIs, connectors, databases, events and automated workflows.\nExperience within legal, financial, professional services or another regulated environment handling confidential information would be advantageous.\nBehavioural Competencies\nEngineering Judgement: Balances AI capabilities with security, maintainability, cost, business requirements and operational risk.\nAnalytical Thinking: Breaks complex requirements into clear, logical and testable components.\nCuriosity & Continuous Learning: Keeps abreast of evolving AI technologies and assesses new tools pragmatically.\nCommunication: Communicates technical concepts, AI limitations, risks and design decisions clearly to technical and non-technical stakeholders.\nCollaboration: Works effectively across business, technology, legal, security, data and external delivery teams.\nConstructive Challenge: Confidently challenges unclear requirements, inappropriate automation and potential risks.\nAttention to Detail: Maintains accurate configurations, testing, permissions, documentation and deployment controls.\nCommercial Awareness: Considers licensing, infrastructure, AI consumption and support costs against expected business value.\nIntegrity & Confidentiality: Protects client, employee and firm information and consistently applies responsible AI principles.\nAccountability: Takes ownership of solution quality, identifies limitations early and drives corrective action where required.\nTechnical Competencies\nAI-Assisted Development: Experience with Claude Code, GitHub Copilot or other approved coding assistants for development, code review, testing, documentation and refactoring.\nAgent & Automation Platforms: Strong working knowledge of Microsoft Copilot Studio, Power Automate, Azure AI services and Azure OpenAI.\nSoftware Engineering: Experience with technologies such as C#, .NET, Python, TypeScript or JavaScript, REST APIs, JSON, SQL, Git, automated testing and CI/CD.\nMicrosoft Technology Stack: Knowledge of Azure, Azure DevOps, Microsoft 365, Microsoft Graph, Power Platform, Key Vault and Application Insights.\nAgent Engineering: Knowledge of prompt and context engineering, tool/function calling, agent state, routing, approval gates, exception handling and agentic workflows.\nRetrieval & Knowledge: Understanding of RAG, embeddings, document ingestion, chunking, metadata, permissions, grounding and source citation.\nAI Evaluation: Experience with evaluation datasets, groundedness and accuracy testing, regression testing, adversarial testing and human review.\nLLMOps & Observability: Understanding of versioning, deployment, rollback, telemetry, tracing, logging, cost monitoring and operational runbooks.\nResponsible AI & Security: Strong understanding of POPIA, confidentiality, legal privilege, data minimisation, access control, auditability, threat modelling and secure software development practices.\nEnterprise Integration: Experience integrating business systems and enterprise platforms through secure APIs, connectors, services and other approved integration methods.","description_format":"text","description_chars":8956,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Continuous learning"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Legal Services"],"lifecycle":[{"event":"open","at":"2026-11-10T14:39:24Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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Docs: https://alion.io/developers"}}