{"id":3103230,"url":"https://alion.io/job/deloitte-contract-machine-learning-operations-engineer","title":"Contract - Machine Learning Operations 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":"A","score":88,"open_postings":13,"ghost_share":0,"stale_share":0.692,"repost_share":0,"time_to_fill_p50_days":12,"computed_at":"2026-10-27T05:46:45Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"contractor","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Birmingham, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":98000,"max_usd":190000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":508},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A2A","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Bitbucket","optional":false},{"name":"CI/CD","optional":false},{"name":"Confluence","optional":false},{"name":"Copilot","optional":false},{"name":"Databricks","optional":false},{"name":"GraphRAG","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Snowflake","optional":false},{"name":"Splunk","optional":false}],"status":"live","first_seen_at":"2026-10-22T18:04:20Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-10-27T03:21:37Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Job Title: Machine Learning Operations Engineer (contract)\nLocation: Hybrid - open to any Deloitte office location (London, Bristol, Birmingham, Manchester, Leeds, Cardiff, Edinburgh, Glasgow). Travel to office 3 days per week\nDuration: 2 months\nContract Start Date: ASAP November 2026\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\nWe are looking for an experienced Machine Learning Operations Engineer to help design, build and deploy enterprise AI and Copilot solutions for one of our Financial Services clients. You will work across machine learning, retrieval-augmented generation (RAG), agent orchestration and cloud platforms to deliver scalable, production-grade AI capabilities. The role requires hands-on experience building ML pipelines, deploying models into production, integrating enterprise data sources and implementing robust governance, monitoring and evaluation frameworks. Preferably someone from a Financial Services background.\nResponsibilities but not limited to;\nDesign and ship production Copilot agents, owning them from prototype through production\nBuild retrieval that holds up in production: Graph RAG combining knowledge-graph traversal over accounts, contacts, products, and prior engagements with vector search,\nDesign agent memory: episodic and semantic memory organized as memory nodes, with recall, summarization, and decay policies tuned per use case,\nOwn organizational context management\nBuild and productionize ML model training pipelines\nBuild batch and online inference for those models\nOwn answer quality as an engineering discipline\nCompose multi-agent workflows using A2A, and integrate tools and data through MCP servers (CRM, client and product reference data, document stores, calendar and email, Databricks, Snowflake, Kubernetes, Confluence, Bitbucket, Splunk).\nBuild and run evals: task-level and end-to-end agent evaluations, regression suites, LLM-as-judge, human review loops with sellers, and quality and safety gating before release.\nClose the loop with production feedback\nProduce the model documentation, test evidence and controls\nDeploy and operate solutions on public cloud (AWS and/or Azure) with strong SDLC, security, resiliency, and observability practices.\nPartner with sales, coverage, product and business partners to turn use cases into shipped, supported agents.\nEssential Skills and Experience\n> 4 years experience building Copilot or AI agent solutions preferably from a Financial Services background\n> 4 years experience building and deploying machine learning models in production environments.\nExperience of Graph RAG and MCP integrations.\nExpertise in ML Ops practices, CI/CD, model monitoring and lifecycle management.\nExperience withGenerative AI, LLMs and R etrieval-Augmented Generation (RAG) architectures.\nStrong Python development skills and experience with Databricks.\nExperience developing batch and real-time inference pipelines.\nKnowledge of vector search, retrieval optimisation and AI evaluation frameworks.\nExperience working with AWS and/or Azure cloud platforms.\nFamiliarity with Kubernetes, Snowflake and enterprise data integrations.\nStrong understanding of security, observability and AI governance principles.\nExcellent stakeholder management and communication skills\nDeliverables: Responsibilities but not limited to:\nDesign, build and own production-grade copilot agents that provide secure, context-aware and persistent support across the client relationship.\nDeliver production ML and retrieval capabilities that turn enterprise data into timely, explainable recommendations for sellers.\nEstablish the evaluation, governance and operational controls required for trusted AI at enterprise scale.\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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