{"id":3796813,"url":"https://alion.io/job/intuit-senior-software-engineer-8","title":"Senior Software Engineer","company":{"id":16628,"name":"Intuit","domain":"intuit.com","url":"https://alion.io/company/intuit","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Radancy","truth_index":{"grade":"B","score":77,"open_postings":48,"ghost_share":0,"stale_share":0.938,"repost_share":0,"time_to_fill_p50_days":42,"computed_at":"2026-11-07T05:47:08Z"}},"role":"Backend","role_family":"Backend","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, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Flink","optional":false},{"name":"Java","optional":false},{"name":"Kotlin","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"RAG","optional":true}],"status":"live","first_seen_at":"2026-11-05T09:48:58Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-07T12:18:05Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"About the role\nOur team is building the contextual knowledge stack: the foundation that captures, structures, and serves customer context so Intuit’s AI experiences understand each customer and act on it responsibly. Two capabilities sit at its core:\nKnowledge authoring. This is how knowledge gets into the stack. We build the tools, schemas, and pipelines that let teams define, curate, validate, and version the knowledge AI agents rely on, including entities, relationships, domain concepts, business rules, and customer facts derived from activity. Authoring has to be easy for product and domain teams, rigorous enough to keep quality and lineage intact, and governed so every piece of knowledge has a clear owner, source, and access policy.\nContext retrieval. This is how knowledge gets to the moment it’s needed. We build the retrieval layer that assembles the right context for an agent or experience in real time: finding relevant knowledge across structured stores, graphs, and vector or search indexes, ranking and filtering it, and honoring privacy and access controls. The result is context that is accurate, fresh, low-latency, and sized to fit within model context limits.\nResponsibilities\nWhat you’ll do: Knowledge authoring\nBuild authoring services, APIs, and schemas for creating, updating, and versioning knowledge, with validation, review workflows, and change history\nDevelop pipelines that derive and enrich knowledge from customer events, documents, and product signals\nImplement quality checks, provenance, and lineage so every knowledge item can be traced to its source\nApply ownership, consent, and fine-grained access controls at authoring time, not as an afterthought\nSenior: Define the knowledge model and ontology strategy, set authoring standards across teams, and drive adoption by partner teams\nWhat you’ll do: Context retrieval\nBuild low-latency retrieval services that combine structured lookups, graph traversal, keyword search, and semantic (vector) search\nImplement ranking, filtering, freshness, and deduplication so agents receive concise, relevant context\nOptimize for latency, cost, and scale, with caching, indexing, and observability for retrieval quality and performance\nBuild evaluation harnesses and metrics (relevance, coverage, latency, staleness) to measure and improve retrieval\nEnforce permissions and privacy rules at query time\nSenior: Own the retrieval architecture and tradeoffs across consistency, latency, and cost, and lead the evaluation strategy\nQualifications\nWhat you’ll bring\nBS/MS in Computer Science or a related field, or equivalent experience\n5+ years of professional software engineering experience, including designing and operating production distributed systems\nStrong programming skills in Java, Python, or Kotlin (or similar), with deep knowledge of data structures, algorithms, and distributed systems fundamentals\nExperience with large-scale data and streaming technologies such as Kafka, Flink, Spark, Cassandra, Iceberg, or graph and search stores\nExperience with cloud infrastructure (AWS preferred), CI/CD, and production operations\nA record of leading projects from ambiguous requirements through launch, with measurable impact\nExcellent written and verbal communication, and the ability to influence across teams\nNice to have\nHands-on experience with LLMs, RAG, vector or graph retrieval, or agent and context-engineering patterns\nBackground in data governance, lineage, or fine-grained access control\nExperience with event-driven architectures, customer data platforms, or customer journey modeling\nExperience mentoring engineers or acting as a tech lead\nIntuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. 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