{"id":3244145,"url":"https://alion.io/job/tessell-solution-architect","title":"Solution Architect","company":{"id":810122,"name":"Tessell","domain":"tessell.com","url":"https://alion.io/company/tessell-3","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Solutions","role_family":"Solutions","seniority":"staff","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":31000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":37},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Datadog","optional":false},{"name":"FinOps","optional":false},{"name":"Grafana","optional":false},{"name":"Helm","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LLM","optional":false},{"name":"Node JS","optional":false},{"name":"OpenAI","optional":false},{"name":"Oracle","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Platform Engineering","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Self-Healing","optional":false},{"name":"Slack","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"VPN","optional":false},{"name":"Weaviate","optional":false},{"name":"JavaScript","optional":true}],"status":"live","first_seen_at":"2026-10-25T18:35:30Z","employer_posted_date":null,"last_verified_at":"2026-10-25T18:35:30Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"We are looking for a Solutions Architect to join the Customer Success Engineering team at Tessell. This is a senior engineering-focused role chartered with two intersecting missions: designing scalable technical solutions for our enterprise customers, and re-engineering how the Customer Success organisation itself operates with AI at the centre. You will build AI-powered tooling, automation, and workflows that make the CS team faster, smarter, and more proactive. You will define the engineering infrastructure behind customer operations from intelligent monitoring and incident triage to self-healing runbooks and data- driven account health dashboards. You will also be the technical authority in customer architecture discussions, bringing design rigour and product depth to every engagement. This role is ideal for someone who thinks like a product engineer but communicates like a customer-success professional, someone who can identify a manual, repetitive process, build the tool that eliminates it, and then scale it across the team.\n\nResponsibilities: \nAI-Powered Customer Success Operations Design and build AI-driven tooling to automate repetitive CS workflows: incident triage, ticket classification, RCA generation, customer health scoring, and escalation routing.\nDevelop LLM-based assistants that help CS engineers debug issues faster by ingesting logs, error traces, and runbooks to surface actionable resolution steps.\nBuild intelligent monitoring and alerting pipelines that detect anomalies in customer environments and proactively surface risks before customers report them.\nCreate AI-assisted billing and cost analysis tools that automate monthly attribution reports, flag cloud spend anomalies, and generate customer-facing cost summaries.\nIntegrate AI tooling into Slack-based CS workflows, enabling contextual search over past incidents, tickets, and resolution playbooks.\nEngineering Tools and Platforms for CS: Own the internal tooling roadmap for the Customer Success team, identify friction points, prioritise build vs. buy, and ship solutions that multiply team capacity.\nBuild and maintain a CS engineering platform: runbook automation, self-service diagnostic tools, environment health dashboards, and resource governance utilities.\nDevelop tooling to enforce environment hygiene at scale: automated detection of long- running POC resources, approval workflow enforcement, and cost guardrails.\nDesign and implement structured data pipelines that aggregate customer environment telemetry for trend analysis, SLA tracking, and capacity planning.\nStandardise and automate deployment validation, onboarding readiness checks, and post-deployment health verification for new customer environments.\nSolutions Architecture for Enterprise Customers: Serve as the technical authority in customer architecture discussions, reviewing connectivity designs, multi-cloud topologies, and database deployment patterns.\nDesign reference architectures for complex customer environments, including private-link topologies, multi-region CP-DP setups, VPN/Transit Gateway configurations, and dedicated host deployments.\nEvaluate and document customer infrastructure requirements across AWS and Azure: local zones, Exadata on Azure, Oracle licensing, custom OS images, and private endpoint design.\nLead architecture reviews for new customers onboarding, ensuring designs are scalable, secure, and aligned with Tessell's platform capabilities.\nContribute to Tessell's internal knowledge base of proven deployment patterns and anti- patterns derived from customer engagements.\nProcess Engineering and Continuous Improvement: Identify and eliminate manual processes across the CS team by building automation, templates, and lightweight internal products.\nEstablish engineering standards for the CS team: code review practices for internal tooling, documentation standards, observability patterns, and deployment pipelines.\nDefine and implement data-driven processes for account health monitoring, proactive risk identification, and customer lifecycle management.\nPartner with SRE and Engineering to close systemic gaps, including Terraform state management, readiness validation, and observability for customer-facing features.\nTechnical Leadership and Collaboration: Act as the engineering bridge between the Customer Success and Product Engineering teams, translating operational pain points into well-scoped engineering requirements.\nMentor CS engineers on debugging methodologies, AI tool usage, and structured problem-solving approaches.\nCollaborate with pre-sales and product teams on evaluating new customer architectures, POC designs, and capability assessments.\nRepresent the voice of CS operations in product planning, advocating for features that improve operational efficiency and customer self-service.\n\nRequirements: \n6-10 years of experience in solutions architecture, platform engineering, or customer success engineering at a cloud or SaaS company.\nStrong hands-on engineering background, comfortable building internal tools, automation scripts, and data pipelines in Python, Node.js, or similar.\nPractical experience applying AI/LLM tools (OpenAI, Anthropic, LangChain, RAG patterns) to automate operational or analytical workflows.\nDeep expertise in AWS and Azure cloud services: networking, compute, storage, managed databases, and observability tooling.\nExperience with infrastructure-as-code (Terraform), Kubernetes/Helm, and CI/CD pipelines.\nDemonstrated ability to take a manual process, design a solution, build it, and roll it out across a team.\nStrong communication skills, able to translate complex technical concepts for both engineering teams and customer stakeholders.\nExperience building AI-powered internal tools: log analysis bots, smart ticketing systems, documentation assistants, or similar.\nFamiliarity with vector databases (Pinecone, Weaviate, pgvector) and embedding-based search for knowledge retrieval.\nBackground in database technologies: Oracle, PostgreSQL, SQL Server, and managed DB services.\nExperience with FinOps practices: cloud cost attribution, billing automation, and spend anomaly detection.\nPrior experience designing multi-tenant DBaaS or platform-as-a-service architectures.\nExposure to observability tooling (Datadog, Prometheus, Grafana) and building alert pipelines for production environments.\nSkills\nanthropic, aws, azure, helm, kubernetes, langchain, llm, node.js, openai, python, rag, 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