{"id":1947013,"url":"https://alion.io/job/caterpillar-product-analytics-solution-architect","title":"Product Analytics Solution Architect","company":{"id":176554,"name":"Caterpillar","domain":"caterpillar.com","url":"https://alion.io/company/caterpillar","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":88,"open_postings":37,"ghost_share":0,"stale_share":0.486,"repost_share":0,"time_to_fill_p50_days":35,"computed_at":"2026-10-27T05:46:45Z"}},"role":"Solutions","role_family":"Solutions","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, 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":36},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"DLP","optional":false},{"name":"Embeddings","optional":false},{"name":"EU AI Act","optional":false},{"name":"FinOps","optional":false},{"name":"Git","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Hybrid Search","optional":false},{"name":"IAM","optional":false},{"name":"LangChain","optional":false},{"name":"Least Privilege","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Node JS","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Threat Modeling","optional":false},{"name":"TypeScript","optional":false},{"name":"JavaScript","optional":true}],"status":"closed","first_seen_at":"2026-10-06T07:31:27Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-23T23:27:55Z","board_verified":false,"closed_at":"2026-10-23T23:27:55Z","days_open":17,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":17},"description":"Career Area:\nTechnology, Digital and DataJob Description:\nYour Work Shapes the World at Caterpillar Inc.\nWhen you join Caterpillar, you're joining a global team who cares not just about the work we do - but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here - we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it.\nJob Description\nThe Solutions Architect is the end-to-end technical owner of an enterprise analytics platform composed of a governed analytics function library, a visual workflow builder, an agentic AI analyst, and a tool/integration layer (e.g., Model Context Protocol servers) that grounds the agent in governed enterprise data.\nThis role owns architectural cohesion, non-functional requirements (performance, scalability, reliability, security, cost), code quality, and delivery across all platform surfaces, and integration with upstream and downstream enterprise systems. As the platform's agentic capabilities mature, the role is the AI Solutions Architect - designing, reviewing, and governing agent, retrieval, evaluation, and safety architecture in line with enterprise architecture, security, and Responsible AI standards.\nWhat You Will Do\nSolution & Platform Architecture\nOwn the end-to-end architecture: data sources → analytics library → tool/integration layer → AI agent & workflow builder.\nDefine scalable, secure, reusable patterns for APIs, agent tools, event flows, and platform extensions; govern API lifecycle (versioning, backward compatibility, deprecation, contract testing).\nOwn non-functional requirements: latency budgets, throughput, availability/SLOs, capacity planning, and resilience (retries, circuit breakers, graceful degradation, DR/BCP).\nGovern integrations with enterprise data sources (telemetry, service, warranty, parts, work orders) and ongoing data-warehouse migrations.\nMaintain the architecture roadmap, reference architectures, and Architecture Decision Records (ADRs); drive build-vs-buy and vendor/tooling evaluations; manage tech debt deliberately.\nData, Retrieval & Context Architecture\nDesign the RAG / knowledge architecture: ingestion, chunking, embeddings, vector/hybrid search, retrieval quality, and freshness.\nOwn the semantic layer, data contracts, and metadata/catalog that let the agent reliably map natural language to governed data.\nDefine agent memory and context architecture (session state, short- vs long-term memory, context-window optimization, semantic caching).\nAI & Agentic Architecture\nArchitect the agent backbone: orchestration/planner-executor patterns, tool boundaries, memory, evaluation hooks, and guardrails; support multi-agent patterns as needed.\nDefine the LLM provider strategy (cloud-hosted, self-hosted, model gateway) and a cost/FinOps architecture: model routing, caching, token economics, and budget guardrails.\nOwn the LLMOps lifecycle architecture: model/prompt versioning and registries, canary/A-B rollout of prompts, model upgrade and deprecation strategy.\nOwn the evaluation & observability architecture: LLM tracing (OpenTelemetry), trace stores, golden datasets, eval-in-CI, online/offline eval, drift and regression gates.\nEstablish AI governance practices: design reviews, risk assessments, Responsible AI alignment, and lifecycle management of agents and prompts.\nSecurity, Trust & Compliance\nOwn the AI threat model: prompt injection, data exfiltration via tools, jailbreaks, output sanitization, and least-privilege tool authorization.\nArchitect multi-tenancy, data scoping, row-level security, PII/DLP, and audit/traceability across data and prompts.\nEnsure alignment with enterprise architecture, IT controls, confidential-data handling, and emerging AI regulatory requirements (e.g., AI Act, model cards, data residency).\nAgile Delivery & Technical Leadership\nLead sprint planning, code reviews, and technical design docs; set coding standards and release cadence.\nStay hands-on 30-50%: build reference implementations, unblock complex tickets, prototype risky areas.\nOwn end-to-end troubleshooting across all platform components and integration points.\nMentor engineers on architecture principles, integration patterns, and AI solution design; contribute to hiring, interviewing, team topology, and onboarding/enablement.\nStakeholder Collaboration & Governance\nServe as the trusted technical advisor to the PM, sponsor, AI Center of Excellence, enterprise IT, and security partners.\nLead or participate in architecture review boards, design-governance forums, and change-impact assessments.\nDefine and report architecture success metrics (reliability, latency, cost-per-insight, eval/accuracy, adoption readiness).\nCommunicate complex architectural concepts and trade-offs to technical and business audiences.\nWhat You Have\nEducation & Experience: Bachelor's or Master's in Computer Science, Engineering, or related field; 8+ years building production software, with at least 3 years as a tech lead or solutions architect on a data, analytics, or AI platform.\nApplication Design & Architecture: Converts business requirements into clean, modular, well-documented technical designs and reference architectures.\nSystem & Technology Integration: Deep knowledge of integrating heterogeneous applications, databases, and platforms, with disciplined API/contract governance.\nSystems & Reliability Engineering: Strong grounding in distributed systems, async patterns, scalability, observability, resilience, and secure-by-design.\nTechnical Troubleshooting: Anticipates, diagnoses, and resolves complex multi-system issues - including non-deterministic AI failure modes - quickly.\nTechnology Advising: Advises product, engineering, and senior leadership on technology trade-offs and risk.\nCore Technical & Functional Skills\nLanguages & Frameworks: Expert-level Python; working knowledge of TypeScript / Node.js for full-stack reviews.\n Cloud: Production experience on AWS and/or Azure (compute, storage, networking, IAM).\n Data Platforms: Cloud data warehouse (e.g., Snowflake, BigQuery, Databricks); strong SQL; data modeling; vector stores and retrieval.\n AI / Agentic: Hands-on with LLMs, agent frameworks (Model Context Protocol, function-calling, LangChain / LlamaIndex / Semantic Kernel / Agent SDKs), RAG, context/memory design, and evaluation harnesses.\nLLMOps & Observability: Model gateways, prompt/agent registries, LLM tracing, eval pipelines, drift detection.\nDevOps: CI/CD pipelines, Git workflows, containerization, IaC concepts.\nSecurity: Secure-by-design mindset; AI-specific threat modeling, authorization/least-privilege, and enterprise confidential-data handling.\nWhat Will Set You Apart\nExperience designing or governing production AI agents, copilots, or AI-enabled enterprise applications at scale.\nStrong grasp of Responsible AI, model governance, ethical AI, and emerging AI regulation.\nPrior architecture role on a multi-tenant analytics platform or developer platform.\nFinOps for LLM/compute - optimizing cost-per-query and inference spend at scale.\nExperience integrating with industrial / engineering data ecosystems.\nRecognized open-source or internal-tools contributions and architecture thought leadership.pdated\nPosting Dates:\nOctober 6, 2026 - October 19, 2026Caterpillar is an Equal Opportunity Employer. Qualified applicants of any age are encouraged to apply\nNot ready to apply? 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