{"id":3170897,"url":"https://alion.io/job/skan-ai-sr-software-engineer","title":"Sr Software Engineer","company":{"id":63247,"name":"Skan AI","domain":"skan.ai","url":"https://alion.io/company/skan-ai","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":{"grade":"A","score":92,"open_postings":8,"ghost_share":0,"stale_share":0.125,"repost_share":0,"time_to_fill_p50_days":81,"computed_at":"2026-10-27T05:46:45Z"}},"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":{"min_usd":29000,"max_usd":67000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":168},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"GCP","optional":false},{"name":"Grafana","optional":false},{"name":"Helm","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Loki","optional":false},{"name":"Mimir","optional":false},{"name":"MinIO","optional":false},{"name":"MySQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prometheus","optional":false},{"name":"Prompt Caching","optional":false},{"name":"Python","optional":false},{"name":"RabbitMQ","optional":false},{"name":"Redis","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-10-23T15:37:19Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-27T13:26:36Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Be at the Forefront of the Agentic AI Revolution\nAt Skan AI, we are pioneering the context engine for human and agentic execution, bringing context from enterprise operators, systems, and processes to power how the world's largest organizations execute their most complex, mission-critical work.\nWhy Skan AI\nWe're in hyper-growth mode at exactly the right moment in history. As enterprises race to adopt agentic AI, we're uniquely positioned to deliver the clear signal they desperately need: a platform that trains and grounds AI Agents in trillions of real execution signals, enabling reliable, compliant automation of their most complex processes.\nBacked by Dell Technologies Capital and other leading investors, we're the only company that can bridge the gap between AI's promise and enterprise reality, making us perfectly positioned to define the agentic era for modern enterprises.\nOur diverse, collaborative team of 250+ innovators is solving category-defining challenges at the intersection of AI, process intelligence, and enterprise work. Diverse perspectives fuel breakthrough thinking, cross-functional collaboration is the norm, and our work directly transforms how Fortune 500 companies operate. We are shaping the future of work itself.\nAbout the Role\nThe Context Graph team develops the systems that turn large volumes of observed enterprise work into structured process knowledge. Much of that work begins as rapidly evolving research code, experimental harnesses, model-backed pipelines, and new algorithmic components. This role is responsible for turning those systems into production software that can be trusted at scale.\nAs a Software Engineer on the Context Graph team, you will own the engineering boundary between research and production. You will take systems that work in controlled development settings and make them reliable under real workloads, real data volumes, real deployment constraints, and real failure conditions.\nThat means much more than writing services. You will design production interfaces around research components, build scalable execution paths, define rigorous testing and validation strategies, trace regressions across complex data pipelines, and make sure changes that improve one part of the system do not quietly degrade another.\nThis responsibility is becoming more important as software is increasingly written with agentic coding systems. Code can now be produced faster than it can be deeply understood. We therefore care disproportionately about engineers who bring judgment, code review discipline, testing rigor, systems understanding, and an instinct for finding where seemingly correct code will fail under pressure.\nThe systems themselves are distributed and heterogeneous. Data may originate from observation software running inside an enterprise environment, move through gateway and processing layers, pass through asynchronous multi-stage pipelines, interact with model-backed services, and ultimately execute across cloud, hybrid-cloud, or fully on-premise deployments.\nYour job is to make that entire path boring in the best possible way: predictable, measurable, testable, scalable, and difficult to break.\nResponsibilities\nProductionize research and agentic systems\nTake research prototypes, algorithmic harnesses, and model-backed pipeline components and turn them into reliable production services.\nDefine clear interfaces, contracts, failure behavior, and operational boundaries around rapidly evolving research code.\nBuild integration and regression suites that allow research teams to iterate quickly without destabilizing existing production behavior.\nReview and harden code produced across both human and agentic development workflows, with particular attention to hidden assumptions, incomplete failure handling, and unintended side effects.\nEstablish patterns for moving experimental capabilities into the existing product line without creating fragile parallel systems.\nBuild and scale distributed services\nDesign, build, and operate production Python microservices.\nBuild asynchronous, message-driven services on RabbitMQ or equivalent systems, including retry semantics, idempotency, backpressure, and failure recovery across multi-stage pipelines.\nDevelop and maintain workflows on our in-house orchestration engine, Temporal, and Airflow.\nDesign and evolve data layers across PostgreSQL or MySQL, MinIO object storage, Redis, and high-volume intermediate artifacts.\nBuild and scale the service layer around LLM and agentic pipelines, including throughput, concurrency, caching, latency, and cost.\nReason about the behavior of services as workloads move from development-scale runs to enterprise-scale execution.\nOwn correctness and regression resistance\nBuild rigorous unit, integration, system, replay, load, and regression testing for complex pipeline behavior.\nDesign scalable test harnesses that exercise research and production systems across realistic workloads, failure modes, and deployment configurations.\nDevelop performance benchmarks for throughput, latency, resource utilization, and queue behavior, and identify limits before customers do.\nTrace regressions across long data paths spanning ingestion, gateways, orchestration, services, model calls, storage, and output generation.\nEstablish validation gates that prevent changes from reaching production when correctness, performance, or reliability has degraded.\nMake failures reproducible enough that complex production issues can be diagnosed rather than merely observed.\nBuild for heterogeneous deployment environments\nBuild and maintain CI/CD pipelines covering build, test, artifact publishing, validation, and deployment.\nWrite and maintain Dockerfiles, container images, Kubernetes manifests, Helm charts, and versioning strategies.\nManage infrastructure as code with Terraform across GCP, Azure, and supported enterprise environments.\nDesign services that behave consistently across cloud, hybrid-cloud, and on-premise deployments.\nOwn environment configuration, secrets management, releases, rollbacks, and deployment troubleshooting.\nAccount explicitly for the constraints that emerge in customer-controlled environments, including network boundaries, resource limits, service dependencies, and restricted infrastructure.\nOperate what you build\nInstrument services with high-quality logs, metrics, and traces, and build the dashboards and alerts needed to understand production behavior.\nUse observability to diagnose system behavior across asynchronous and distributed execution paths.\nSupport GPU-hosted and other resource-intensive workloads, including scheduling, resource allocation, and cost control.\nParticipate in production support and on-call for the services your team owns.\nTreat operability as a design requirement rather than something added after deployment.\nCollaboration\nWork directly with applied scientists and AI engineers to understand the intended behavior of new algorithms before productionizing them.\nWork with product engineers and platform teams to integrate new Context Graph capabilities into the existing product without creating regressions.\nParticipate deeply in technical design, code review, testing strategy, and architecture discussions.\nAct as a quality bar for systems entering production, especially when code spans multiple teams or has been developed rapidly through agentic coding workflows.\nRequirements\nWe screen for comparable experience and strong fundamentals, not exact tool matches. Equivalent technologies are welcome.\nAmbitious and Hungry. “Far and away the best prize that life offers is the chance to work hard at work worth doing.” - Theodore Roosevelt\nProduction Backend Experience. 2-5 years building and operating production backend systems in distributed or service-oriented environments.\nStrong Python. You write clear, tested, production-quality Python and can quickly understand unfamiliar code well enough to challenge its assumptions, failure behavior, and scalability.\nSystem Design Fundamentals. You are comfortable reasoning about horizontal and vertical scaling, autoscaling, load balancing, capacity planning, caching, statelessness, concurrency, and throughput versus latency tradeoffs.\nDistributed Systems Fundamentals. You understand asynchronous and event-driven systems, queue-based load leveling, idempotency, retries, timeouts, backpressure, graceful degradation, and how failures propagate across service boundaries.\nTesting and Software Quality. You have strong instincts around unit, integration, system, replay, performance, and regression testing. You know that high coverage is not the same as high confidence and can identify which invariants and failure modes actually need protection.\nPerformance Engineering. You can reason about bottlenecks across CPU, memory, storage, networking, queues, database access, and model-backed services, and design tests that reveal scaling limits before they become production incidents.\nMessage Queues in Production. Experience with RabbitMQ, Kafka, SQS, Pub/Sub, or equivalent event-driven infrastructure, including consumer lag, ordering, retry behavior, and failure recovery.\nData Layer Fundamentals. Strong understanding of relational schema design, query performance, transactions, connection management, caching, and object-storage-backed data flows.\nContainers and Orchestration. You understand how services are packaged, scheduled, scaled, isolated, given resource limits, and operated across different deployment environments.\nDeployment and Infrastructure. Experience with CI/CD and infrastructure as code, and an understanding of the differences between cloud-native, hybrid-cloud, and on-premise production environments.\nOperability. You can debug live systems using logs, metrics, traces, dashboards, and raw system behavior, and are comfortable reasoning through failures that span multiple services.\nEngineering Judgment. You are skeptical of code that merely works once. You look for hidden coupling, untested assumptions, scalability limits, and ways a change can create regressions elsewhere in the system.\nPreferred\nDirect experience with any of the following is valuable:\nWorkflow orchestration engines such as Temporal or Airflow, or experience building similar systems.\nHigh-throughput APIs, gateways, rate limiting, connection pooling, and latency tuning.\nObject storage as a primary data plane, including S3, GCS, or MinIO.\nLLM application infrastructure including model gateways, prompt caching, token and cost management, and model observability.\nLarge-scale data pipelines involving images, event streams, or unstructured artifacts.\nObservability systems such as Grafana, Loki, Tempo, Prometheus, or Mimir.\nMulti-cloud or hybrid-cloud deployments across GCP, Azure, or enterprise-controlled environments.\nOn-premise software deployment and operating systems under constrained infrastructure or network conditions.\nGPU workload scheduling and cost optimization.\nExperience building performance, replay, or regression harnesses for complex distributed systems.\nYou Might Be a Great Fit If You\nAre uncomfortable merging code you do not deeply trust, even when it appears to work.\nThink about failure modes, invariants, and ten-times scale before a service reaches production.\nLike being the engineer who can take a fast-moving research system and make it safe for the rest of the company to depend on.\nFind a subtle regression across five services more interesting than a straightforward greenfield API.\nCare about tests that prove behavior rather than tests that merely increase coverage.\nEnjoy owning a service through design, deployment, observability, production debugging, and on-call.\nAre interested in the new engineering problem created by agentic coding: software can be generated extremely quickly, so judgment, validation, and systems rigor become more valuable, not less.\nSkan AI is an equal opportunity employer committed to 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