обновлено 22 дня назад
AI Solution Architect
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
AI Solution Architect (AI/LLM): Designing and delivering enterprise AI systems, including AI-native applications, agentic workflows, RAG pipelines, and AI-ready data foundations with an accent on production architecture, enterprise integration, and measurable business value. Focus on designing tool-calling agents, building reliable vector and knowledge systems, integrating AI with systems of record, and driving governed AI adoption across the SDLC.
Location: Remote
Company
is a software development outsourcing company delivering cost-efficient product and technology solutions across EdTech, Fintech, eCommerce, and Pharma.
What you will do
- Design end-to-end enterprise AI architectures aligned with business goals.
- Architect copilots, RAG and document intelligence systems, conversational AI, workflow automation, and multi-agent solutions.
- Design AI agents with tool calling, memory, orchestration, and human-in-the-loop controls.
- Build AI-ready data foundations using vector stores, semantic search, knowledge graphs, metadata, and RAG pipelines.
- Integrate AI with ERP, CRM, HRIS, ITSM, finance, and collaboration systems through APIs and event streams.
- Set architecture standards, govern delivery, mentor engineers, engage clients, and measure AI adoption across the SDLC.
Requirements
- 8+ years of experience in solution or software architecture, including 3+ years delivering production AI solutions.
- Hands-on experience with a major LLM platform such as OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, or Azure OpenAI.
- Practical experience with an agentic framework such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, or OpenAI Agents SDK.
- Strong Python and experience with at least one major cloud platform: AWS, Azure, or GCP.
- Experience building RAG systems, working with vector databases, and integrating APIs with SQL/NoSQL systems and enterprise patterns.
- Excellent communication skills for explaining technical tradeoffs to executives, engineers, and clients.
Nice to have
- TypeScript or JavaScript for full-stack and agent-tooling work.
- Knowledge graphs, data lakes or lakehouses, and metadata or semantic modeling.
- Experience with GitHub Copilot, Claude Code, Cursor, LLMOps, evaluation, guardrails, and observability.
- Familiarity with AI governance, security, compliance, data residency, PII handling, and model risk.
- Consulting or client-facing delivery experience.
Culture & Benefits
- Fully remote work with flexible hourly-based freelance opportunities.
- Project-based work within a professional and welcoming freelance team.
- Opportunity to contribute to AI adoption across ’s engineering practice.
- Work focused on production systems and measurable improvements in adoption, cost, and delivery cycle time.
Hiring process
- Recruiting interview lasting 30–45 minutes.
- Technical interview lasting 1–1.5 hours, followed by an optional client interview of up to one hour.
- Reference check and offer; the process usually takes up to two weeks.
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