出海雷达

Technical Architect- Remote, India

AI开发工程限定地区(需当地身份)
公司LeewayHertz
薪资未公开
工作地点United States
地域资格限定地区(需当地身份)
时区要求日间重叠约 9 小时,基本正常作息
用工类型Full Time
发布时间今天
数据来源Himalayas
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注意地域限制:该职位明确限定在 United States 招聘。如果你是位于中国大陆的求职者,通常需要当地工作身份才能投递,或需与雇主确认是否接受独立合同(Contractor)形式合作。
关于本页中文:职位描述由本机离线模型翻译,属机器翻译,供快速判断用;正式沟通与投递请以英文原文为准。官方翻译通道恢复后会自动重译升级。

这是一个远程职位。我们正在寻找一个拥有10多年的技术经验的技术建筑师,包括生产生成人工智能系统的设计和交付经验证明。理想候选人将结合大型语言模型,检索增强代码和 Agentic 架构的深入操作控制与判断力来设计 AI 解决方案,这些解决方案能够承受成本,延迟,安全和合规性等企业实际限制。这个角色拥有 AI 端到端的技术架构:在发现和预售期间,将解决方案塑造,定义参考架构和集成模式,选择模型和平台,并通过实施指导交付团队。你将直接与客户利益相关者,产品经理和工程领导人合作,并帮助制定我们 AI 实践的技术标准。这是一个高级的手动建筑角色而不是纯粹的咨询职责。这个角色拥有从发现到生产的多个同时 AI 参与。除了技术架构之外,技术建筑师预计将作为客户的可靠顾问,领导工程团队,导师未来技术领袖,并通过实践参与推动工程卓越。要求责任 · 在检索,编排,模型,集成和部署层面上拥有 GenAI 解决方案的端到端架构。· 将模糊业务问题转化为人工智能解决方案设计,具有明确范围,可行性评估和成功指标。·定义参考架构,设计模式和 RAG,代理工作流和 LLM 的可重复使用集成加速器。· 领导模型和平台选择,记录每个决定背后的成本,延迟,准确性和数据居住权之间的交易。·设计非功能包裹:可扩展性,延迟预算,可用性,可观测性和推断成本控制。· 建筑数据和检索管道涵盖摄入量,零碎,嵌入策略,矢量商店选项和混合搜索。·定义评估策略和防护屏幕,以便能够衡量并管理精度,基层性,安全性和幻觉率。· 将安全性,隐私和合规性纳入设计中:PII 处理,租隔离,访问控制和审计。·通过解决方案研讨会,努力估计,技术建议和客户演示支持预售和发现。·指导提供

查看英文原文

This is a remote position.

We are seeking a Technical Architect with 10+ years of overall technology experience, including proven experience architecting and delivering production Generative AI systems. The ideal candidate will combine deep hands-on command of large language models, retrieval-augmented generation, and Agentic architectures with the judgement to design AI solutions that hold up under real enterprise constraints of cost, latency, security, and compliance. This role owns the technical architecture of AI engagements end-to-end: shaping the solution during discovery and pre-sales, defining reference architectures and integration patterns, selecting models and platforms, and guiding delivery teams through implementation. You will work directly with client stakeholders, product managers and engineering leads, and will help set the technical standards of our AI practice. This is a senior, hands-on architecture role rather than a purely advisory one. This role owns multiple concurrent AI engagements from discovery through production. Beyond technical architecture, the Technical Architect is expected to act as a trusted advisor to clients, lead engineering teams, mentor future technical leaders, and drive engineering excellence through hands-on involvement.
Requirements

Responsibilities
· Own the end-to-end architecture of GenAI solutions across the retrieval, orchestration, model, integration, and deployment layers.

  • Translate ambiguous business problems into AI solution designs with clear scope, feasibility assessment and success metrics.
  • Define reference architectures, design patterns and reusable accelerators for RAG, agentic workflows and LLM integration.
  • Lead model and platform selection, documenting the cost, latency, accuracy and data-residency trade-offs behind each decision.
  • Design the non-functional envelope: scalability, latency budgets, availability, observability and inference cost control.
  • Architect data and retrieval pipelines covering ingestion, chunking, embedding strategy, vector store selection and hybrid search.
  • Define evaluation strategy and guardrails so accuracy, groundedness, safety and hallucination rates can be measured and governed.
  • Embed security, privacy and compliance into the design: PII handling, tenancy isolation, access control and audit.
  • Support pre-sales and discovery through solution workshops, effort estimation, technical proposals and client presentations.
  • Guide delivery teams, run design reviews and mentor engineers, while staying hands-on in prototyping and unblocking hard problems.
  • Maintain architecture documentation and decision records, and assess which advances in generative AI are ready for enterprise adoption.
  • Own multiple client engagements simultaneously while maintaining delivery quality.
  • Lead discovery workshops, challenge assumptions, and refine business requirements into technically sound solutions.
  • Push back on unrealistic timelines, architectures, or requirements using engineering judgement and data.
  • Build strong relationships with Team, product owners, and executive stakeholders.
  • Mentor senior engineers and cultivate future architects and technical leaders.
  • Lead architectural governance, design reviews, and technical decision records.
  • Set engineering standards, coding guidelines, AI development best practices, and review critical code.
  • Remain hands-on by building prototypes, solving difficult technical problems, and contributing production-quality code when needed.
  • Drive cross-project reuse through internal frameworks, accelerators, and reference implementations.
  • Present architecture, trade-offs, risks, and implementation strategy confidently to executive audiences.

Essential Skills

Job
· 10+ years in software engineering, data or AI roles, including at least 3 years in an architect or technical lead capacity.

  • Demonstrated experience architecting and delivering production Generative AI systems, not only prototypes or POCs.
  • Strong hands-on Python, with the ability to prototype designs and review production code.
  • Deep expertise in LLM application architecture: prompt and context engineering, structured output, tool calling and orchestration.
  • Proven experience designing RAG systems end-to-end, including chunking, embedding selection, hybrid retrieval and re-ranking.
  • Proven ability to manage multiple enterprise AI programs simultaneously.
  • Strong client-facing consulting experience with executive communication.
  • Excellent presentation, whiteboarding, and workshop facilitation skills.
  • Demonstrated experience influencing technical decisions across multiple teams.
  • Experience managing senior engineers and mentoring future technical leaders.
  • Strong engineering judgement balancing quality, cost, delivery timelines, and business value.
  • Comfortable making architectural decisions with incomplete information.
  • Experience with agent and orchestration frameworks such as LangChain, LangGraph, LlamaIndex or CrewAI.
  • Strong knowledge of vector databases (Pinecone, Weaviate, Qdrant, FAISS, pgvector) and their operational trade-offs.
  • Solid machine learning and deep learning fundamentals, including fine-tuning and adaptation approaches (LoRA/QLoRA, PEFT).
  • Strong cloud architecture skills on AWS, Azure or GCP, including their AI/ML and data services.
  • Experience with microservices, API design, event-driven patterns and enterprise system integration.
  • Working knowledge of MLOps and LLMOps: CI/CD, containerization, model versioning, monitoring and rollback.
  • Experience defining LLM evaluation and observability approaches (RAGAS, LangSmith, DeepEval or equivalent).
  • Personal
  • Strong communication and stakeholder management skills, including with non-technical and client-side audiences.
  • Sound engineering judgement, with the confidence to defend a design and the openness to revise it.
  • Strong ownership across the full delivery lifecycle, not only the design phase.
  • Ability to mentor engineers and lead through influence rather than authority.
  • Comfortable operating with ambiguity in a fast-moving technology space.

Personal
· Strong communication and stakeholder management skills, including with non-technical and client-side audiences.

  • Sound engineering judgement, with the confidence to defend a design and the openness to revise it.
  • Strong ownership across the full delivery lifecycle, not only the design phase.
  • Ability to mentor engineers and lead through influence rather than authority.
  • Comfortable operating with ambiguity in a fast-moving technology space

Preferred Skills

Job
· Experience architecting multi-agent systems and complex autonomous workflows.

  • Exposure to multimodal AI covering vision, speech or document understanding.
  • Experience with inference optimization and serving at scale (vLLM, TensorRT-LLM, Triton, quantization).
  • Experience deploying open-weight models on-premise or in-VPC for data-sensitive clients.
  • Knowledge of graph-based retrieval (GraphRAG) and knowledge-graph modelling.
  • Familiarity with AI governance and responsible AI frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001).
  • Experience with data platform architecture and pipelines (Airflow, dbt, Spark, lakehouse patterns).
  • Pre-sales, solutioning or client-facing consulting experience in a services organisation.
  • Domain depth in one or more of BFSI, healthcare, retail, supply chain or manufacturing.
  • Personal
  • Proactive mindset with a genuine interest in tracking a fast-moving field.
  • Consulting orientation, balancing technical ideals against client timelines and budgets.

Personal
· Proactive mindset with a genuine interest in tracking a fast-moving field.

  • Consulting orientation, balancing technical ideals against client timelines and budgets.

Other Relevant Information

  • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
  • Relevant certifications in AI/ML, cloud architecture (AWS/Azure/GCP), or enterprise architecture are a plus.
  • A portfolio of production Generative AI architectures, open-source contributions or published work is highly desirable.

Benefits

  • This role offers the flexibility of working remotely in India.

LeewayHertz is an equal opportunity employer and does not discriminate based on race, colour, religion, sex, age, disability, national origin, sexual orientation, gender identity, or any other protected status. We encourage a diverse range of applicants.

Originally posted on Himalayas

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