Senior AI Engineer

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    "description_html": "<strong>Job Brief:</strong><br>We are looking for AI Engineer to build and operate the AI engine — the multi-agent system, retrieval layer, knowledge ingestion, evaluation infrastructure. The lead builder of the AI core. <br><br><strong>VentureDive Overview:</strong><br>Founded in 2012 by veteran technology entrepreneurs from MIT and Stanford, VentureDive is the fastest-growing technology company in the region that develops and invests in products and solutions that simplify and improve the lives of people worldwide. We aspire to create a technology organization and an entrepreneurial ecosystem in the region that is recognized as second to none in the world.<br><br><strong>Responsibilities:</strong><ul><li>Build and operate the multi-agent system: orchestrator, specialist agents, Critic </li><li>Build the retrieval layer: vector search, knowledge graph queries, hybrid retrieval, citation generation </li><li>Build knowledge ingestion pipelines: source connectors, entity extraction, document linking, temporal versioning </li><li>Build the evaluation pipeline that measures every generation </li><li>Integrate with model APIs and govern through an AI gateway </li></ul><br><strong>Required Qualifications & Experience:</strong><ul><li>5+ years engineering, with last 2+ years on production GenAI or agent-based systems </li><li>Has shipped at least one AI product to real users with real production traffic — not a demo </li><li>Strong applied skills: LLM orchestration, RAG, agent frameworks, prompt engineering, evaluation </li><li>Deep familiarity with at least one vector store and one agent framework, opinionated about trade-offs </li><li>Cross-stack: Python, cloud infrastructure, containers, observability, data pipelines </li><li>Strong evaluation instincts — can talk in detail about how they measured groundedness, where their system failed, what they did </li><li>Pragmatic on model choice, cost, latency </li></ul><br><strong>Nice-to-have</strong><ul><li>Multi-agent systems specifically (orchestrator/critic patterns, not just single-agent RAG) </li><li>Knowledge graphs, ontology-driven retrieval, hybrid graph + vector </li><li>Databricks AI tooling (Mosaic AI, Vector Search, Lakebase, Agent Bricks, MLflow) </li><li>ML platform background, not just applied AI </li><li>Open-source contributions to AI tooling </li></ul><br>What we look for beyond required skills<br>In order to thrive at VentureDive, you<br>…are intellectually smart and curious<br>…have the passion for and take pride in your work<br>…deeply believe in VentureDive’s mission, vision, and values<br>…have a no-frills attitude<br>…are a collaborative team player<br>…are ethical and honest<br><br>Are you ready to put your ideas into products and solutions that will be used by millions?<br>You will find VentureDive to be a quick pace, high standards, fun and a rewarding place to work at. Not only will your work reach millions of users world-wide, you will also be rewarded with competitive salaries and benefits. If you think you have what it takes to be a VenDian, come join us ... we're having a ball!<br>#LI-Hybrid",
    "description_text": "Job Brief:\nWe are looking for AI Engineer to build and operate the AI engine — the multi-agent system, retrieval layer, knowledge ingestion, evaluation infrastructure. The lead builder of the AI core.\n VentureDive Overview:\nFounded in 2012 by veteran technology entrepreneurs from MIT and Stanford, VentureDive is the fastest-growing technology company in the region that develops and invests in products and solutions that simplify and improve the lives of people worldwide. We aspire to create a technology organization and an entrepreneurial ecosystem in the region that is recognized as second to none in the world.\n Responsibilities: Build and operate the multi-agent system: orchestrator, specialist agents, Critic\n Build the retrieval layer: vector search, knowledge graph queries, hybrid retrieval, citation generation\n Build knowledge ingestion pipelines: source connectors, entity extraction, document linking, temporal versioning\n Build the evaluation pipeline that measures every generation\n Integrate with model APIs and govern through an AI gateway\n Required Qualifications & Experience: 5+ years engineering, with last 2+ years on production GenAI or agent-based systems\n Has shipped at least one AI product to real users with real production traffic — not a demo\n Strong applied skills: LLM orchestration, RAG, agent frameworks, prompt engineering, evaluation\n Deep familiarity with at least one vector store and one agent framework, opinionated about trade-offs\n Cross-stack: Python, cloud infrastructure, containers, observability, data pipelines\n Strong evaluation instincts — can talk in detail about how they measured groundedness, where their system failed, what they did\n Pragmatic on model choice, cost, latency\n Nice-to-have Multi-agent systems specifically (orchestrator/critic patterns, not just single-agent RAG)\n Knowledge graphs, ontology-driven retrieval, hybrid graph + vector\n Databricks AI tooling (Mosaic AI, Vector Search, Lakebase, Agent Bricks, MLflow)\n ML platform background, not just applied AI\n Open-source contributions to AI tooling\nWhat we look for beyond required skills\nIn order to thrive at VentureDive, you\n…are intellectually smart and curious\n…have the passion for and take pride in your work\n…deeply believe in VentureDive’s mission, vision, and values\n…have a no-frills attitude\n…are a collaborative team player\n…are ethical and honest\nAre you ready to put your ideas into products and solutions that will be used by millions?\nYou will find VentureDive to be a quick pace, high standards, fun and a rewarding place to work at. Not only will your work reach millions of users world-wide, you will also be rewarded with competitive salaries and benefits. If you think you have what it takes to be a VenDian, come join us ... we're having a ball!\n#LI-Hybrid",
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      "datePosted": "2026-05-13",
      "description": "<strong>Job Brief:</strong><br>We are looking for AI Engineer to build and operate the AI engine — the multi-agent system, retrieval layer, knowledge ingestion, evaluation infrastructure. The lead builder of the AI core. <br><br><strong>VentureDive Overview:</strong><br>Founded in 2012 by veteran technology entrepreneurs from MIT and Stanford, VentureDive is the fastest-growing technology company in the region that develops and invests in products and solutions that simplify and improve the lives of people worldwide. We aspire to create a technology organization and an entrepreneurial ecosystem in the region that is recognized as second to none in the world.<br><br><strong>Responsibilities:</strong><ul><li>Build and operate the multi-agent system: orchestrator, specialist agents, Critic </li><li>Build the retrieval layer: vector search, knowledge graph queries, hybrid retrieval, citation generation </li><li>Build knowledge ingestion pipelines: source connectors, entity extraction, document linking, temporal versioning </li><li>Build the evaluation pipeline that measures every generation </li><li>Integrate with model APIs and govern through an AI gateway </li></ul><br><strong>Required Qualifications & Experience:</strong><ul><li>5+ years engineering, with last 2+ years on production GenAI or agent-based systems </li><li>Has shipped at least one AI product to real users with real production traffic — not a demo </li><li>Strong applied skills: LLM orchestration, RAG, agent frameworks, prompt engineering, evaluation </li><li>Deep familiarity with at least one vector store and one agent framework, opinionated about trade-offs </li><li>Cross-stack: Python, cloud infrastructure, containers, observability, data pipelines </li><li>Strong evaluation instincts — can talk in detail about how they measured groundedness, where their system failed, what they did </li><li>Pragmatic on model choice, cost, latency </li></ul><br><strong>Nice-to-have</strong><ul><li>Multi-agent systems specifically (orchestrator/critic patterns, not just single-agent RAG) </li><li>Knowledge graphs, ontology-driven retrieval, hybrid graph + vector </li><li>Databricks AI tooling (Mosaic AI, Vector Search, Lakebase, Agent Bricks, MLflow) </li><li>ML platform background, not just applied AI </li><li>Open-source contributions to AI tooling </li></ul><br>What we look for beyond required skills<br>In order to thrive at VentureDive, you<br>…are intellectually smart and curious<br>…have the passion for and take pride in your work<br>…deeply believe in VentureDive’s mission, vision, and values<br>…have a no-frills attitude<br>…are a collaborative team player<br>…are ethical and honest<br><br>Are you ready to put your ideas into products and solutions that will be used by millions?<br>You will find VentureDive to be a quick pace, high standards, fun and a rewarding place to work at. Not only will your work reach millions of users world-wide, you will also be rewarded with competitive salaries and benefits. If you think you have what it takes to be a VenDian, come join us ... we're having a ball!<br>#LI-Hybrid",
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