{"schemaVersion":"jobsearcher.job.v1","id":"faa01d0fadb407bc603b1982","url":"https://jobsearcher.com/jobs/faa01d0fadb407bc603b1982","canonicalUrl":"https://jobsearcher.com/jobs/faa01d0fadb407bc603b1982","title":"AI Solution Architect","description":"Qualification:\n\nWe are seeking a AI Solutions Architect with strong experience in designing and developing enterprise-grade AI platforms, RAG pipelines, multi-agent systems, and cloud-native AI solutions. The ideal candidate will be responsible for architecting scalable Generative AI and Agentic AI applications, developing intelligent agent workflows, implementing AI governance and security controls, and delivering production-ready AI solutions across cloud environments. This role requires a strong focus on AI architecture, agent orchestration, retrieval optimization, evaluation, observability, automation, and collaboration with cross-functional teams to deliver secure, scalable, reliable, and high-quality AI solutions.\n\nWhat You'll Bring\n\n12-14 years of experience in software engineering, AI/ML engineering, data engineering, or related fields, with strong hands-on experience building enterprise AI solutions.\nStrong experience designing and developing Generative AI and Agentic AI applications using foundation models, RAG architectures, and agent-based workflows.\nStrong programming and application development experience using Python, FastAPI, and related frameworks.\nExperience designing high-performance RAG pipelines, vector search solutions, embedding workflows, semantic caching, and context-window optimization.\nStrong experience with multi-agent architectures, agent orchestration, memory management, autonomous planning, and stateful workflows.\nStrong understanding of AI evaluation, observability, hallucination reduction, responsible AI, data privacy, governance, and security.\nAbility to collaborate effectively with software engineers, data engineers, data scientists, architects, product teams, and business stakeholders.\nExperience designing scalable, production-ready AI platforms and reusable enterprise architecture patterns.\n\nPreferred Qualification\n\nExperience with LangChain, LangGraph, and Amazon Bedrock Runtime.\nExperience with Model Context Protocol (MCP) and Agent-to-Agent (A2A) interoperability standards.\nExperience with vector databases such as Milvus and Amazon Aurora PostgreSQL.\nExperience implementing CI/CD pipelines and DevOps practices for AI/ML applications.\nExperience with Docker, Kubernetes, and cloud-native application deployment.\nKnowledge of AI evaluation frameworks, tracing, observability, and monitoring of model and agent performance.\nExperience with AWS services including Amazon Bedrock, Lambda, EKS, SageMaker, S3, RDS, and DocumentDB.\nKnowledge of responsible AI practices including bias mitigation, model risk management, privacy, compliance, and AI safety guardrails.\nExperience optimizing AI applications for latency, scalability, reliability, cost, and token consumption.\n\nSkills Required:\n\nGenerative AI, Agentic AI, RAG, Python\n\nRole:\n\nWhat You'll Do\n\nDesign, develop, and maintain scalable Generative AI and Agentic AI platforms using cloud-native architectures.\nDesign and implement high-performance Retrieval-Augmented Generation (RAG) pipelines, semantic search, embedding workflows, and vector database integrations.\nDevelop agentic workflows supporting autonomous planning, reasoning, decision-making, tool use, and function calling.\nDesign and implement multi-agent collaboration patterns including supervisor-worker, decentralized collaboration, and stateful graph architectures.\nDevelop agent memory and state-management capabilities using frameworks such as LangChain, LangGraph, and Amazon Bedrock Runtime.\nImplement structured data extraction, schema-driven outputs, context optimization, and intelligent retrieval strategies.\nIntegrate vector databases such as Milvus and Amazon Aurora PostgreSQL to support high-performance similarity search and retrieval.\nBuild data and embedding pipelines using Python, FastAPI, Apache Spark, and related technologies.\nDevelop and deploy AI applications using AWS services including Amazon Bedrock, Lambda, EKS, SageMaker, S3, RDS, and DocumentDB.\nImplement AI evaluation frameworks, tracing, observability, and monitoring to measure agent performance, latency, token usage, errors, and failure states.\nDevelop guardrails to reduce hallucinations and improve the reliability, safety, and accuracy of AI-generated outputs.\nImplement governance, security, data privacy, compliance, bias mitigation, and responsible AI practices across AI platforms.\nImplement CI/CD pipelines and automated testing strategies for non-deterministic AI applications and model outputs.\nContainerize and orchestrate AI workloads using Docker and Kubernetes.\nCollaborate with cross-functional teams to define enterprise AI architectures, reusable design patterns, and production deployment strategies.\nEvaluate emerging Generative AI, Agentic AI, interoperability, and cloud technologies and recommend improvements to existing AI solutions.\n\nExperience:\n\n12 to 14 years\n\nJob Reference Number:\n\n14101","company":"Impetus Technologies","rawCompany":"impetus technologies","city":"Chicago","state":"IL","isRemote":false,"isActive":false,"createdAt":"2026-09-10T11:41:43.564Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"AI Solution Architect","description":"Qualification:\n\nWe are seeking a AI Solutions Architect with strong experience in designing and developing enterprise-grade AI platforms, RAG pipelines, multi-agent systems, and cloud-native AI solutions. The ideal candidate will be responsible for architecting scalable Generative AI and Agentic AI applications, developing intelligent agent workflows, implementing AI governance and security controls, and delivering production-ready AI solutions across cloud environments. This role requires a strong focus on AI architecture, agent orchestration, retrieval optimization, evaluation, observability, automation, and collaboration with cross-functional teams to deliver secure, scalable, reliable, and high-quality AI solutions.\n\nWhat You'll Bring\n\n12-14 years of experience in software engineering, AI/ML engineering, data engineering, or related fields, with strong hands-on experience building enterprise AI solutions.\nStrong experience designing and developing Generative AI and Agentic AI applications using foundation models, RAG architectures, and agent-based workflows.\nStrong programming and application development experience using Python, FastAPI, and related frameworks.\nExperience designing high-performance RAG pipelines, vector search solutions, embedding workflows, semantic caching, and context-window optimization.\nStrong experience with multi-agent architectures, agent orchestration, memory management, autonomous planning, and stateful workflows.\nStrong understanding of AI evaluation, observability, hallucination reduction, responsible AI, data privacy, governance, and security.\nAbility to collaborate effectively with software engineers, data engineers, data scientists, architects, product teams, and business stakeholders.\nExperience designing scalable, production-ready AI platforms and reusable enterprise architecture patterns.\n\nPreferred Qualification\n\nExperience with LangChain, LangGraph, and Amazon Bedrock Runtime.\nExperience with Model Context Protocol (MCP) and Agent-to-Agent (A2A) interoperability standards.\nExperience with vector databases such as Milvus and Amazon Aurora PostgreSQL.\nExperience implementing CI/CD pipelines and DevOps practices for AI/ML applications.\nExperience with Docker, Kubernetes, and cloud-native application deployment.\nKnowledge of AI evaluation frameworks, tracing, observability, and monitoring of model and agent performance.\nExperience with AWS services including Amazon Bedrock, Lambda, EKS, SageMaker, S3, RDS, and DocumentDB.\nKnowledge of responsible AI practices including bias mitigation, model risk management, privacy, compliance, and AI safety guardrails.\nExperience optimizing AI applications for latency, scalability, reliability, cost, and token consumption.\n\nSkills Required:\n\nGenerative AI, Agentic AI, RAG, Python\n\nRole:\n\nWhat You'll Do\n\nDesign, develop, and maintain scalable Generative AI and Agentic AI platforms using cloud-native architectures.\nDesign and implement high-performance Retrieval-Augmented Generation (RAG) pipelines, semantic search, embedding workflows, and vector database integrations.\nDevelop agentic workflows supporting autonomous planning, reasoning, decision-making, tool use, and function calling.\nDesign and implement multi-agent collaboration patterns including supervisor-worker, decentralized collaboration, and stateful graph architectures.\nDevelop agent memory and state-management capabilities using frameworks such as LangChain, LangGraph, and Amazon Bedrock Runtime.\nImplement structured data extraction, schema-driven outputs, context optimization, and intelligent retrieval strategies.\nIntegrate vector databases such as Milvus and Amazon Aurora PostgreSQL to support high-performance similarity search and retrieval.\nBuild data and embedding pipelines using Python, FastAPI, Apache Spark, and related technologies.\nDevelop and deploy AI applications using AWS services including Amazon Bedrock, Lambda, EKS, SageMaker, S3, RDS, and DocumentDB.\nImplement AI evaluation frameworks, tracing, observability, and monitoring to measure agent performance, latency, token usage, errors, and failure states.\nDevelop guardrails to reduce hallucinations and improve the reliability, safety, and accuracy of AI-generated outputs.\nImplement governance, security, data privacy, compliance, bias mitigation, and responsible AI practices across AI platforms.\nImplement CI/CD pipelines and automated testing strategies for non-deterministic AI applications and model outputs.\nContainerize and orchestrate AI workloads using Docker and Kubernetes.\nCollaborate with cross-functional teams to define enterprise AI architectures, reusable design patterns, and production deployment strategies.\nEvaluate emerging Generative AI, Agentic AI, interoperability, and cloud technologies and recommend improvements to existing AI solutions.\n\nExperience:\n\n12 to 14 years\n\nJob Reference Number:\n\n14101","datePosted":"2026-09-10T11:41:43.564Z","dateModified":"2026-09-10T11:41:43.564Z","hiringOrganization":{"@type":"Organization","name":"Impetus Technologies","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Chicago","addressRegion":"IL","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"faa01d0fadb407bc603b1982"},"url":"https://jobsearcher.com/jobs/faa01d0fadb407bc603b1982"}}