{"id":1450964,"url":"https://alion.io/job/simplify-hr-proprietary-limited-senior-ml-engineer","title":"Senior ML Engineer","company":{"id":1891733,"name":"Simplify HR","domain":"simplify.hr","url":"https://alion.io/company/simplify-hr","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":{"grade":"B","score":75,"open_postings":386,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":33,"computed_at":"2026-10-12T06:55:09Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","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":{"min_usd":29000,"max_usd":72000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1507},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"SAS","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"closed","first_seen_at":"2026-09-04T00:00:00Z","employer_posted_date":"2026-09-04","last_verified_at":"2026-10-03T15:35:23Z","board_verified":false,"closed_at":"2026-10-03T15:35:23Z","days_open":29,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":29},"description":"Our client in the banking industry is looking for a Senior ML Engineer with strong hands-on experience in productionising and scaling Machine Learning, AI and Generative AI solutions. The ideal candidate should have a strong software/cloud engineering background, with expertise in Databricks, MLflow, Azure Kubernetes Service (AKS), Python, Docker, Kubernetes and MLOps.\nThe role will focus on taking ML and Data Science solutions from prototype through to production, building AI Agents, GenAI and RAG applications, deploying open-source LLMs, developing APIs and microservices and ensuring solutions are scalable, secure, monitored and reliable.\n\nKey Responsibilities\nProductionise, deploy and monitor machine learning models and data science pipelines on Databricks.\nBuild, deploy and support AI Agents, GenAI applications and RAG solutions on Databricks.\nDevelop and maintain reusable ML pipelines using MLOps principles, including CI/CD, automated testing, monitoring and governance.\nDeploy, optimise and manage open-source AI and machine learning models on Azure Kubernetes Service (AKS).\nDesign, develop and support custom APIs and microservices on AKS to expose AI and machine learning capabilities to business applications.\nImplement containerised solutions using Docker and Kubernetes to ensure scalable, secure and resilient deployments.\nMonitor model performance, drift, reliability and operational health in production environments.\nPartner with Data Scientists to productionise prototypes and enable business-ready solutions.\nCollaborate with platform, security, cloud and infrastructure teams to ensure compliance with enterprise standards.\nTroubleshoot and resolve production issues related to models, pipelines, APIs and AI applications.\nOptimise AI and ML solutions for performance, scalability, cost and reliability.\nContribute to engineering standards, reusable frameworks and best practices across the AI and ML ecosystem.\nMentor junior engineers and promote knowledge sharing across the team.\nStay current with advancements in AI, GenAI, MLOps, Databricks, Kubernetes and cloud technologies.\nCore Deliverables\nProduction-ready ML models and pipelines running on Databricks.\nAI Agents and business applications deployed on Databricks.\nOpen-source LLMs and AI services deployed on AKS.\nSecure and scalable APIs exposing AI capabilities to consuming systems.\nAutomated deployment, monitoring and governance processes.\nReliable, scalable and compliant AI platforms supporting business outcomes.\nKey Skills\nDatabricks Workflows, Model Serving, MLflow and Mosaic AI.\nAzure Kubernetes Service (AKS).\nPython, SQL and REST APIs.\nDocker and Kubernetes.\nCI/CD and MLOps practices.\nMachine Learning and Generative AI.\nLLM deployment and optimisation.\nCloud engineering and infrastructure automation.\nMonitoring, observability and troubleshooting.\nQualifications\nComputer Science, Engineering, Econometrics, Mathematical Statistics, Actuary Science Masters or Doctorate will be an added advantage.\nPreferred Certifications\nMicrosoft Azure certifications (AZ-104, AZ-305, AI-102 or equivalent).\nDatabricks certifications (Data Engineer, Machine Learning Engineer, Generative AI Engineer).\nKubernetes and containerisation certifications (CKA, CKAD or equivalent).\nDevOps, MLOps or Platform Engineering certifications.\nAWS or Google Cloud certifications will be advantageous.\nMachine Learning, Artificial Intelligence or Data Science certifications from recognised providers such as Microsoft, Databricks, SAS, Coursera or DeepLearning.AI will be an added advantage.\nTechnical/Professional Knowledge\nStrong understanding of MLOps, DevOps and software engineering practices for machine learning platforms.\nExperience building, deploying and supporting machine learning solutions in production environments.\nProficiency in Python and experience with SQL and API development.\nExperience with Databricks, MLflow, Model Serving and cloud-native AI/ML platforms.\nHands-on experience with Kubernetes, Docker and containerised application deployment.\nExperience deploying and supporting machine learning and Generative AI solutions on Azure Kubernetes Service (AKS).\nKnowledge of CI/CD pipelines, infrastructure automation and platform monitoring.\nExperience with distributed computing technologies such as Spark and large-scale data processing frameworks.\nUnderstanding of machine learning, large language models (LLMs), retrieval-augmented generation (RAG) and AI agents.\nAbility to productionise data science solutions and collaborate effectively with Data Scientists.\nExperience delivering end-to-end AI and machine learning use cases from development to production.\nAbility to translate technical concepts into business outcomes and communicate effectively with stakeholders.\nStrong written and verbal communication skills with the ability to work across cross-functional teams.\nSelf-driven, adaptable and capable of thriving in a fast-paced, technology-driven 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ATS"],"lifecycle":[{"event":"open","at":"2026-09-29T07:47:14Z"},{"event":"close","at":"2026-10-03T15:35:23Z"}],"visa":[],"liveness":null,"pay":null,"html_url":"https://alion.io/job/simplify-hr-proprietary-limited-senior-ml-engineer","json_url":"https://alion.io/job/simplify-hr-proprietary-limited-senior-ml-engineer.json","meta":{"generated_at":"2026-10-12T20:42:50Z","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 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