{"id":710309,"url":"https://alion.io/job/agilebridge-ai-engineer","title":"AI Engineer","company":{"id":669131,"name":"Agile Bridge","domain":"agilebridge.co.za","url":"https://alion.io/company/agilebridge","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"BambooHR","truth_index":{"grade":"D","score":50,"open_postings":12,"ghost_share":0.833,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-02T06:13:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","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":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"C#","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false}],"status":"live","first_seen_at":"2026-04-29T00:00:00Z","employer_posted_date":"2026-04-29","last_verified_at":"2026-11-02T09:55:55Z","board_verified":true,"closed_at":null,"days_open":187,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":187},"description":"Purpose of the Role:\nAgile Bridge is looking for an AI Engineer who can turn promising models into secure, scalable and dependable AI capabilities. You will work at the intersection of machine learning, deep learning and software engineering: designing model architectures, training and evaluating models, integrating them into software systems and improving their performance in production.\nWhat to expect in this role\nThis role is model- and engineering-focused. A project may require you to select and adapt a neural-network architecture, develop a custom prediction or representation capability, benchmark a foundation model, optimise inference performance, or package a validated model behind a reliable service. You will be expected to consider the full production context: data quality, model behaviour, latency, throughput, resource use, cost, security and maintainability.\nYou will collaborate with Data Scientists on scientific validity and evaluation, Software and Applied AI Engineers on product integration, and Platform teams on deployment and observability. The role is distinct from a purely analytical Data Scientist position and from an Agentic AI role centred on flows, agents and business-process orchestration.\nWhat you will do\nTranslate product and technical requirements into model objectives, architecture decisions and measurable acceptance criteria.\nSelect, implement and train machine learning, deep learning, transformer or foundation-model approaches suited to the problem.\nPrepare training and evaluation data, including preprocessing, feature or representation engineering and dataset versioning.\nRun controlled experiments, tune models and evaluate accuracy, robustness, generalisation and computational efficiency.\nPackage model-backed capabilities as maintainable services, APIs, libraries or platform components.\nBuild or contribute to CI/CD and MLOps pipelines for testing, registration, release and environment promotion.\nDeploy and monitor AI workloads, responding to drift, latency, failures, resource constraints and cost concerns.\nEmbed security, responsible AI, traceability and clear technical documentation throughout the model lifecycle.\nResearch emerging methods and adopt them only where benchmarking demonstrates meaningful value.\nWhat you need\nA bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Mathematics or a related quantitative field.\nTypically 3-5 years' relevant experience building and operationalising machine learning or deep learning capabilities.\nStrong Python capability and evidence of sound software-engineering practice.\nHands-on experience developing, evaluating and improving machine learning or deep learning models.\nExperience integrating model-backed capabilities into a software or production environment.\nThe judgement to balance model quality with latency, scalability, reliability, security, maintainability and cost.\nCore technical skills\nWe are looking for depth in the core engineering areas, not superficial exposure to every framework. Comparable tools are welcome where they demonstrate the same capability.\nProgramming and software engineering: Strong Python, object-oriented design, testing, debugging, dependency management and clean, maintainable code. SQL is required; C# or Java is useful for integration-heavy environments.\nMachine learning and deep learning: Strong fundamentals across supervised and unsupervised learning, neural networks, loss functions, optimisation, regularisation and generalisation.\nFrameworks: Practical experience with PyTorch, TensorFlow, scikit-learn or equivalent frameworks. Transformer or foundation-model experience should include evaluation and adaptation, not only API consumption.\nModel evaluation and optimisation: Experiment design, meaningful metrics, benchmarking, hyperparameter optimisation, error analysis, robustness testing and performance profiling.\nData and storage: Data preprocessing, feature or representation engineering, dataset versioning, and experience with relational or non-relational databases.\nIntegration: Building or consuming REST APIs, service contracts and model-serving patterns that connect AI capabilities to wider software systems.\nMLOps and delivery: Git, Docker, CI/CD, model versioning or registries, reproducible environments and monitoring of model and service performance.\nSecurity and governance: Privacy and PII protection, secrets and access management, traceability, responsible-AI evaluation and secure model deployment.\nUseful, but not essential\nAn Honours or Master's degree in Artificial Intelligence, Machine Learning, Computer Science or a related field.\nExperience with MLflow or an equivalent experiment-tracking and model-registry platform.\nExposure to Azure, AWS or Google Cloud AI services and infrastructure-as-code practices.\nExperience with Kubernetes, GPU workloads, distributed training or inference optimisation techniques such as batching, quantisation or model compression.\nPractical experience evaluating or adapting generative models, including transformers, embeddings and retrieval components.\nHow you work\nYou think in systems and understand how model behaviour affects the wider product.\nYou experiment rigorously and distinguish benchmark improvement from real operational value.\nYou communicate technical trade-offs, uncertainty and limitations honestly.\nYou collaborate across modelling, software, data and platform disciplines.\nYou remain curious without introducing new technology merely for novelty.\nThe opportunity\nYou will join an innovation-focused engineering environment where AI capabilities are expected to survive real users, real data and real production constraints. The role offers exposure to model development, production AI, responsible engineering and emerging model architectures while working alongside experienced software, data and platform teams.\nHow success will be measured\nModel and architecture choices are appropriate to the use case and supported by evidence.\nAI capabilities meet agreed quality, latency, reliability, security and cost requirements.\nExperiments, code, data dependencies and model artefacts are reproducible and maintainable.\nProduction performance remains visible, with risks and deterioration addressed early.\nCustomers and engineering teams can understand the capability, constraints and operational requirements.","description_format":"text","description_chars":6423,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-11T04:36:32Z"}],"visa":[],"liveness":{"score":4,"band":"cold","label":"Long shot","p_open":1,"p_active":0.149,"p_room":0.28,"age_days":187,"expected_fill_days":30,"reasons":["conf:1","stale_co","ghost","win:tail","crowd:"],"computed_at":"2026-11-02T06:13:15Z"},"pay":null,"html_url":"https://alion.io/job/agilebridge-ai-engineer","json_url":"https://alion.io/job/agilebridge-ai-engineer.json","meta":{"generated_at":"2026-11-02T10:50:32Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":15069,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-03T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":669131},"rest":"https://alion.io/mcp/rest/get_company?id=669131"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fagilebridge-ai-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fagilebridge-ai-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fagilebridge-ai-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/agilebridge-ai-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fagilebridge-ai-engineer"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":710309}},{"tool":"search_jobs","arguments":{"company":"agilebridge.co.za","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":710309},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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