{"id":4068416,"url":"https://alion.io/job/datatech-recruitment-ai-engineer-aws-generative-ai","title":"AI Engineer (AWS / Generative AI)","company":{"id":4533131,"name":"DataTech Recruitment","domain":null,"url":"https://alion.io/company/datatech-recruitment","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cape Town, South Africa"],"countries":["ZA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Bedrock AgentCore","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Strands Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"Feature Store","optional":false},{"name":"Git","optional":false},{"name":"Go","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Terraform","optional":false},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-11-11T11:52:44Z","employer_posted_date":null,"last_verified_at":"2026-11-11T11:52:44Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"The Role\n\n Building AI solutions is one thing. Getting them into production, making them work reliably and delivering real value to a business is another.\n\n We’re looking for an experienced AI Engineer who enjoys doing both.\n\n You’ll join an established AWS cloud services business, working with clients across different industries to design, build and deploy practical AI, machine learning and Generative AI solutions.\n\n This is a hands-on engineering role with a strong client-facing element. You’ll be involved from the first discovery conversation through solution design, development, deployment and ongoing improvement.\n\n You won’t be sitting on the sidelines drawing up concepts for someone else to build. You’ll write production-quality code, make technical decisions and take ownership of delivery.\n\n There’s also room to grow, whether your future lies in senior technical leadership or leading projects and engineers.\n\n Core Tech: Python, AWS, Amazon Bedrock, Amazon SageMaker, AWS Lambda, APIs, Terraform, CI/CD, GenAI, RAG and Agentic AI.\n\nResponsibilities\n\n Design, develop, test and deploy AI, ML and Generative AI solutions using AWS technologies.\n\n Build GenAI applications using Amazon Bedrock, including prompt workflows, embeddings, retrieval-based solutions and AI agents.\n\n Develop and deploy machine learning pipelines using Amazon SageMaker.\n\n Write clean, maintainable Python code and build APIs to integrate AI capabilities into business applications.\n\n Develop and maintain infrastructure as code to support reliable deployments.\n\n Participate in client discovery sessions, understand business challenges and identify where AI can deliver measurable value.\n\n Progress towards independently leading technical assessments and client workshops.\n\n Evaluate solution options, technical feasibility, costs and potential risks.\n\n Help translate client requirements into practical project plans, estimates and deliverables.\n\n Take ownership of assigned projects from development through deployment, monitoring and optimisation.\n\n Work closely with engineers and delivery teams to resolve technical challenges and maintain delivery standards.\n\n Contribute to reusable solutions, technical documentation and engineering best practices.\n\n Share knowledge and support other engineers as the team grows.\n\nRequirements\n\n We’re looking for someone who has moved beyond experimenting with AI and has practical experience building solutions that work in real environments.\n\nEssential experience:\n\n 3+ years of hands-on experience in AI engineering, applied machine learning or software development with substantial practical AI exposure.\n\n Strong Python development skills, including writing production-quality applications.\n\n Practical experience implementing machine learning solutions using Amazon SageMaker.\n\n At least 1 year of hands-on experience with Amazon Bedrock, including retrieval-based solutions and agentic workflows.\n\n Experience taking at least one ML or GenAI solution from experimentation into production or near-production, including post-deployment support.\n\n Sound understanding of ML fundamentals, including data preparation, feature engineering, model selection, evaluation and monitoring.\n\n Experience developing and integrating APIs for AI and ML applications.\n\n Experience with AWS Lambda, particularly Python-based workloads.\n\n Familiarity with AWS services supporting knowledge bases and retrieval solutions.\n\n Experience using Terraform or similar infrastructure-as-code tools.\n\n Good working knowledge of Git, CI/CD pipelines and Linux environments.\n\n Ability to explain technical solutions, limitations and trade-offs to both technical and non-technical stakeholders.\n\n Confidence engaging directly with clients, understanding their requirements and contributing to technical discovery discussions.\n\n Ability to manage competing priorities and take ownership of deliverables.\n\nAdvantageous experience:\n\n Leading AI/ML client assessments, workshops and technical discovery engagements.\n\n Experience in consulting, technical pre-sales, solution scoping or preparing project estimates.\n\n Exposure to multiple client projects or industry environments.\n\n Amazon Bedrock AgentCore, Strands, MCP integrations or other agentic AI frameworks.\n\n Advanced SageMaker capabilities, including model tuning, pipelines, feature stores and model monitoring.\n\n Vector databases and retrieval technologies such as Amazon OpenSearch, PostgreSQL with pgvector, S3 Vectors or Amazon RDS.\n\n Containerised deployments using Amazon ECS or EKS.\n\n AWS AI services such as Rekognition, Transcribe, Polly or 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