{"id":2137196,"url":"https://alion.io/job/deloitte-contract-python-ai-engineer","title":"Contract - Python AI Engineer","company":{"id":272,"name":"Deloitte","domain":"deloitte.com","url":"https://alion.io/company/deloitte","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Avature","truth_index":{"grade":"B","score":80,"open_postings":10,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":16,"computed_at":"2026-10-10T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":84000,"max_usd":221000,"period":"year","method":"role_country_seniority_unknown","sample_n":131},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"OpenAI","optional":false},{"name":"Pydantic","optional":false},{"name":"Pydantic AI","optional":false},{"name":"Pytest","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"LangChain","optional":true}],"status":"live","first_seen_at":"2026-10-09T02:11:11Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-11T01:49:38Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Job Title: Python AI Engineer\nLocation: London\nDuration: 6 months\nContract Start Date: 12 October\nDay Rate: Competitive (Inside IR35)\nDeloitte\nWorking with the Deloitte Associate (Contractor) Programme means we can offer you the opportunity to work on a variation of industry and client related projects. Our aim is to retain the best talent and so when your project end date nears our team of Talent Community Advisors will be working with you to look at alternative projects within the firm that suit your experience should you wish to continue with Deloitte.\nThe Role\nSuccessful candidate will develop modular Python services across question planning, AI workflow orchestration, information retrieval, evidence processing, LLM integration, response generation and validation. You will work collaboratively with engineering, data, platform and evaluation specialists to translate AI concepts and experiments into reliable, scalable and maintainable production services.\nThis is a hands-on software engineering role requiring strong production Python experience and practical delivery of Generative AI and RAG systems.\nKey Responsibilities\nDesign, develop and maintain production-grade Python services for Generative AI and complex RAG solutions.\nDefine typed application, API and model contracts.\nIntegrate LLMs using structured outputs, tool calling, schema validation, context management and controlled fallback mechanisms.\nImplement multi-stage retrieval using vector search, lexical search, hybrid retrieval, metadata filtering, reranking and evidence selection.\nBuild asynchronous services with appropriate timeout, retry, cancellation and exception-handling controls.\nDevelop automated unit, integration, contract, regression and failure-path tests.\nImplement structured logging, tracing and operational metrics across AI workflows.\nMonitor and optimise response quality, latency, token usage, cost, concurrency and memory utilisation.\nTranslate evaluation findings and production issues into measurable improvements and regression tests.\nContribute to technical design, code reviews, engineering standards and production support.\nEssential Skills and Experience\nStrong commercial experience developing production-grade Python applications and services.\nAdvanced Python skills, including asynchronous programming, type annotations, modular design, exception handling, profiling and performance optimisation.\nStrong hands-on experience with Generative AI orchestration frameworks (Pydantic, LangGraph, MS Agent Framework/Semantic Kernel) including typed models, nested schemas, custom validation, serialisation and schema-constrained LLM outputs.\nDemonstrable experience delivering Generative AI and RAG solutions beyond the proof-of-concept stage.\nStrong understanding of embeddings, vector, graph and lexical search, hybrid retrieval, query decomposition, chunking, metadata filtering, reranking and evidence selection.\nExperience integrating LLMs using structured outputs, function or tool calling, prompt and model versioning, token management, response validation and fallback strategies.\nExperience building multi-step, agentic or workflow-based AI applications.\nStrong automated testing experience using Pytest or equivalent frameworks.\nExperience with APIs, distributed services, Git, CI/CD, containers and production observability.\nExperience implementing structured logging, distributed tracing and operational monitoring.\nStrong troubleshooting skills across application behaviour, retrieval quality, model execution, concurrency, memory usage and performance.\nTechnical Skillsets\nExperience with Azure OpenAI, Azure AI Search or equivalent AI and enterprise search platforms.\nFamiliarity with Pydantic AI, LangGraph, Semantic Kernel or similar orchestration frameworks.\nExperience with LLM and RAG evaluation approaches (Azure toolset preferred: Azure Foundry, ML Studio etc.).\nKnowledge of retrieval-quality metrics and controlled experiment design.\nExperience with Docker, Kubernetes, telemetry and load testing.\nKnowledge of knowledge graphs, temporal retrieval or document-linkage techniques.\nExperience developing AI solutions within regulated or specialist knowledge domains.\nUnderstanding of Generative AI monitoring and protection frameworks (preventing prompt injection, misuse and monitoring costs, tokens, update etc.)\nIR35\nAs a means of managing tax, commercial and reputational risks, Deloitte prohibits the use of Associates through Personal Service Companies (‘PSCs’). 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