{"id":120520,"url":"https://alion.io/job/cognite-senior-software-engineer","title":"Senior Software Engineer","company":{"id":4475,"name":"Cognite","domain":"cognite.com","url":"https://alion.io/company/cognite","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":10,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":null,"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":18000,"max_usd":42000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":139},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"C++","optional":false},{"name":"CI/CD","optional":false},{"name":"FastAPI","optional":false},{"name":"GCP","optional":false},{"name":"Go","optional":false},{"name":"Grafana","optional":false},{"name":"Honeycomb","optional":false},{"name":"Java","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kotlin","optional":false},{"name":"Kubernetes","optional":false},{"name":"NLP","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Redis","optional":false},{"name":"TypeScript","optional":false},{"name":"Embeddings","optional":true},{"name":"Fine-tuning","optional":true},{"name":"RAG","optional":true},{"name":"Semantic Search","optional":true},{"name":"Semantic Search","optional":true}],"status":"live","first_seen_at":"2026-08-25T03:39:03Z","employer_posted_date":null,"last_verified_at":"2026-08-25T03:39:03Z","board_verified":false,"closed_at":null,"days_open":54,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":54},"description":"We're seeking a Senior Software Engineer who excels at building high-performance distributed systems and thrives in a fast-paced startup environment. You'll be working on cutting-edge data infrastructure challenges that directly impact how Fortune 500 industrial companies manage their most critical operational data.\nResponsibilities:\nPlatform Ownership: Design, build, and operate the core serverless execution engine and workflow orchestration layer that serve as foundational primitives for CDF's AI and automation capabilities.\nReliability Engineering: Own uptime, latency SLOs, and incident response for platform services, ensuring functions execute deterministically and workflows progress without data loss or silent failures.\nScalability: Architect for multi-tenant and multi-cloud, high-throughput workloads. Design scheduling, queueing, and retry mechanisms that degrade gracefully under pressure.\nAPI Design: Define and evolve clean API-first architecture, versioned REST, and event-driven APIs that downstream engineering teams and external customers depend on.\nObservability: Instrument services with distributed tracing, structured logging, and alerting (OpenTelemetry / Prometheus / Grafana / Honeycomb stack) so failures surface before customers notice.\nCI/CD and Testing: Champion test automation, unit, integration, and smoke testsand maintain deployment pipelines that ship to production with confidence.\nPerformance: Profile and resolve bottlenecks in execution throughput, cold-start latencies, and cross-service call chains driving a snappy platform experience for industrial workloads.\nCost Efficiency (Bonus): Model compute and storage costs for function execution; identify and implement optimizations that reduce cloud spend without sacrificing reliability.\nRequirements:\n6-8 Years of Engineering: Proven track record building and operating production backend services at scale.\nExpertise: Deep mastery of JVM languages (Kotlin preferred, Java acceptable), Python (FastAPI), distributed systems patterns, and cloud-native service design (Kubernetes, Azure, GCP, AWS, Private cloud).\nWorkflow and Orchestration: Hands-on experience with workflow engines (Conductor, Apache Airflow, or equivalent) and event-driven architectures (Kafka, Pub/Sub).\nData and Storage: Comfortable working with relational databases (PostgreSQL) and non-relational databases, object storage (Data Lakes), and caching layers (Redis) in multi-tenant environments.\nObservability Stack: Practical experience with OpenTelemetry, Prometheus, and Grafana for instrumentation and operational insight.\nML Platform Exposure: Experience supporting ML workloads and notebooks in production, whether through job scheduling, resource management, experiment tracking integration, or model serving infrastructure.\nContextualization Domain (Bonus): Familiarity with industrial knowledge graph construction, entity resolution, or NLP/CV pipelines as they relate to industrial asset data is a strong differentiator.\nFull-Stack Awareness (Bonus): Familiarity with React or TypeScript is a plus for consuming and dogfooding your own platform's developer tooling.\nThe Platform Thinking Spirit: A passion for building composable, well-documented, and automated platform systems that empower other engineers, including ML engineers, to build faster.\nGood to have:\nML Workload Support: Build and extend platform primitives for compute scheduling, environment management, and secrets handling that enable ML engineers to run model training, fine-tuning, and batch inference jobs reliably.\nContextualization Pipelines: Support the engineering infrastructure behind Cognite's contextualization capabilities (entity matching, asset hierarchy inference, and P& ID parsing) by ensuring the platform can orchestrate long-running, GPU-aware, and data-intensive ML workflows without manual intervention.\nVector and Embedding Infrastructure (Bonus): Familiarity with serving or storing vector embeddings to support semantic search and RAG-based contextualization use cases.\nModel Lifecycle Awareness: Understand model versioning, A/B experiment tracking, and the boundary between platform concerns and ML framework concerns, so the platform stays lean while ML teams stay unblocked.","description_format":"text","description_chars":4250,"description_truncated":false,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Industrial IoT (IIoT)","Manufacturing & Industrial 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