{"id":3481773,"url":"https://alion.io/job/esko-artificial-intelligence-engineer","title":"Artificial Intelligence Engineer","company":{"id":2667541,"name":"Esko","domain":"escospb.ru","url":"https://alion.io/company/escospb","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"HeadHunter","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, 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both agents and deterministic validators can use them, and so that humans can read, curate and correct them. \n\nBuild and operate platform services for ingesting, curating, retrieving, and versioning knowledge, ensuring it is scoped correctly to the relevant customer and workflow context. \n\nDefine how semantic retrieval, deterministic validation, and human review work together, including how applicability, uncertainty, conflicting evidence, and escalation are handled. \n\nDesign and implement reusable APIs and tools that allow AI systems and deterministic services to apply, validate, and explain contextual knowledge. \n\nBuild evaluation suites covering retrieval quality, applicability, conflict and ambiguity handling, and explanation quality, using measurable results as the primary evidence of progress. \n\nTake end-to-end ownership of substantial technical areas, including their security, tenant isolation, reliability, observability, performance, and compatibility for consuming teams. \n\nWork directly with domain experts and product engineering teams to turn informal expertise into structured, testable knowledge with clear provenance, citation, and release practices. \n\nWhat we are looking for \n\nStrong software engineering experience designing, building, and operating production systems in Python, TypeScript, Java, or a similar language. \n\nAt least 8 years’ experience in a software engineering role, preferably including ownership of a platform, API or other shared-services product used across multiple internal teams. \n\nExperience building knowledge, retrieval, or rule systems that other teams depend on, and the judgement to choose the right formalism for a problem, whether that is a schema, decision table, rules engine, graph, or retrieval. \n\nPractical experience with RAG, information extraction, and structured validation (JSON Schema, Pydantic, or equivalent). \n\nSound judgement when reasoning about applicability, exceptions, missing evidence, and ambiguity, including recognizing when a system should decline to answer or require human review. \n\nExperience constructing evaluation datasets, and the ability to write clearly about modelling decisions, tradeoffs, and failure modes. \n\nUndergraduate, Master’s degree or PhD in Computer Science / Machine Learning / Data Science / Artificial Intelligence, or related disciplines. \n\nComfort operating at the boundary between a platform team and the domain engineering teams that consume it, balancing shared needs and central objectives against individual team priorities and timelines. \n\nExcellent written and verbal communication, with the ability to work fluently with both technical and business stakeholders alike. \n\nPreferred experience \n\nSemantic and symbolic tooling (RDF/OWL, SHACL, Datalog, DMN, production rule engines, policy-as-code). \n\nAgentic systems with tools calling and validation-before-action. \n\nMulti-tenant SaaS with strict isolation and audit requirements. 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