{"id":3467780,"url":"https://alion.io/job/nutanix-ai-agent-engineer","title":"AI Agent Engineer","company":{"id":43912,"name":"Nutanix","domain":"nutanix.com","url":"https://alion.io/company/twitter-handle","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Jobvite","truth_index":{"grade":"A","score":100,"open_postings":7,"ghost_share":0,"stale_share":0,"repost_share":0.286,"time_to_fill_p50_days":9,"computed_at":"2026-10-30T05:48:23Z"}},"role":"AI/ML","role_family":"AI/ML","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":["Belgrade, Serbia"],"countries":["RS"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":59000,"max_usd":155000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2365},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentforce","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Claude","optional":false},{"name":"Claude Code","optional":false},{"name":"Copilot Studio","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Google BigQuery","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenAI Agents SDK","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Apex","optional":true},{"name":"LangChain","optional":true},{"name":"MuleSoft","optional":true},{"name":"n8n","optional":true},{"name":"Power Automate","optional":true},{"name":"Slack","optional":true},{"name":"Streamlit","optional":true}],"status":"live","first_seen_at":"2026-10-29T15:54:36Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-10-30T09:42:10Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Hungry, Humble, Honest, with Heart.\nThe Opportunity\nNutanix Revenue Operations is seeking a hands-on AI Systems Engineer to turn sales, partner, and customer-success use cases into working AI prototypes in days, then harden the ones that prove value into production tools.\nAbout the team\nYou will sit on the Revenue Operations AI team and work directly with sellers, sales leaders, deal desk, forecasting, partner operations, and IT. You will build agents, reusable skills, and data connections across multiple vendor platforms, connecting internal systems (CRM, CPQ, data warehouse, knowledge bases, collaboration tools) with external data sources. You will be measured by working software in users' hands and the time it saves the revenue organization, not by slide decks.\nYour role\nYour core job is to take a revenue use case from a conversation to a working prototype within days, prove its value with real users and data, and then either harden it or retire it.\nRapid prototyping of AI use cases\nWork closely with our AI Product Manager to engage with our sales org, to scope use cases, define the success metric, and ship a working prototype within days. \nAgents and reusable skills across platforms\nDesign and build agents and reusable skills on multiple vendor platforms, choosing the right tool per use case. \nPackage recurring workflows as reusable skills, prompts, and templates that sellers and other builders can run themselves. \nData connectivity, internal and external\nConnect agents to internal systems of record such as CRM, CPQ, billing and bookings, the data warehouse, product telemetry, support cases, and knowledge bases. \nWork with MCP servers, APIs, and connectors that provide agents governed, permission-aware access to revenue data. \nIntegrate external sources such as intent and enrichment providers, firmographic data, earnings filings, and news, and reconcile them with internal account records. \nRequired qualifications\n5 + years in software engineering, data engineering, solutions engineering, or a similar hands-on technical role, 2 years building LLM-powered applications or agents. \nShipped at least five AI agents or LLM workflows used by real business users on real data, not demos only. Be ready to walk us through one end to end. \nStrong Python and SQL. Comfortable with REST APIs, webhooks, OAuth, JSON, and at least one cloud platform (AWS, Azure, or GCP). \nHands-on experience with at least 3 agentic or skill-building platforms: Salesforce Agentforce, Microsoft Copilot Studio, Google Gemini Enterprise, Anthropic Claude (Skills, MCP, Claude Code), OpenAI (Agents SDK, custom GPTs), or open frameworks such as LangGraph. \nExperience connecting AI systems to enterprise data: CRM, data warehouses (Snowflake, Databricks, or BigQuery), document stores, and third-party APIs, including retrieval design (chunking, embeddings, vector search). \nPractical understanding of evaluation, prompt and context design, tool calling, and the security issues specific to agents, such as prompt injection and over-permissioned tools. \nAbility to translate a sales or operations problem into a scoped technical build, and to explain trade-offs to non-technical leaders in plain language. \nExcellent verbal and written English communication skills. \nBachelor's degree in Computer Science, Engineering, Data Science, or equivalent practical experience. \nPreferred qualifications\nExperience in or alongside a Revenue Operations, Sales Operations, or GTM Systems team at an enterprise software company. \nWorking knowledge of Salesforce data models (opportunities, accounts, quotes, forecasts) and CPQ or quote-to-cash processes. \nWork with and operated an MCP server or comparable tool layer that gives agents governed access to a business system. \nExperience with integration and automation platforms such as Workato, MuleSoft, n8n, or Power Automate. \nExperience with conversation intelligence (Gong or similar), enrichment providers (ZoomInfo, 6sense, Clay), and enterprise search (Glean or similar) \nBuilt a lightweight front end (Streamlit, React, or Slack and Teams apps) so non-technical users can run agents without training. \nFamiliarity with hybrid cloud and infrastructure concepts, which helps in understanding Nutanix products and customers. 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