Job Description

About Brillio:

Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Brillio, renowned for its world-class professionals, referred to as "Brillians", distinguishes itself through their capacity to seamlessly integrate cutting-edge digital and design thinking skills with an unwavering dedication to client satisfaction.

Brillio takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. Brillio's relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work® certification year after year.

Architect

Primary Skills

• AWS Networking, AWS Kenesis, AWS Elastic Cache, AWS PaaS Services, AWS EKS, AWS Cognito, Solution Architecture - AWS, AWS Redshift

Job requirements

• Job Title: AI / Agentic AI Architect
• Role: Architect – Agentic AI Platforms & Applications
• Location: NY
• Work Type: Remote with occasional travel
• Job Summary
• We are looking for an experienced AI / Agentic AI Architect to design, build, and scale enterprise-grade AI agents, autonomous workflows, and LLM-powered applications. The role combines hands-on AI engineering, solution architecture, cloud-native modernization, and stakeholder advisory to move high-value AI use cases from rapid experimentation to secure, governed, production-ready deployment.
• Key Responsibilities
• • Define agentic AI reference architectures across LLM applications, RAG pipelines, tool/function calling, memory systems, multi-agent orchestration, and enterprise integrations.
• • Design and deploy AI agents and autonomous workflows for business functions such as Finance, Legal, Operations, Sales, Support, and Growth.
• • Evaluate foundation models and enterprise AI platforms including Gemini, Vertex AI, OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, and open-source LLMs.
• • Integrate AI solutions with enterprise platforms such as Google Workspace, Slack, CRM systems, internal APIs, databases, and knowledge repositories.
• • Implement observability, evaluation, guardrails, monitoring, reliability controls, and responsible AI practices for production applications.
• Required Skills & Experience
• • 8+ years of software engineering, solution architecture, enterprise architecture, or platform engineering experience.
• • 2+ years of hands-on experience building AI, GenAI, LLM-powered, or agentic applications; production deployment experience preferred.
• • Strong understanding of LLMs, RAG, prompt engineering, vector databases, tool/function calling, context management, memory systems, and AI workflow orchestration.
• • Hands-on experience with frameworks such as LangChain, LangGraph, CrewAI, Google ADK, AutoGen, Semantic Kernel, or similar.
• • Strong engineering skills in Python and one or more of Java, Go, Node.js, React, TypeScript, APIs, microservices, SQL/NoSQL, and event-driven systems.
• • Experience with Google Cloud, AWS, or Azure, including Docker, Kubernetes, CI/CD, DevSecOps, and cloud-native deployment patterns.
• Nice to Have Skills
• • Experience with AI evaluation frameworks, observability tools, guardrails, and feedback loops.
• • Knowledge of fine-tuning, model optimization, open-source LLM deployment, multi-agent coordination, and autonomous decision-making systems.
• • Experience with Pinecone, Weaviate, Chroma, Vertex AI Vector Search, Google Workspace APIs, Slack integrations, or enterprise automation tools.
• • Exposure to AI-driven SDLC tools, cloud certifications, open-source AI contributions, or fast-paced innovation environments.
• Impact & Value
• • Enable clients to move from AI experimentation to secure, scalable, production-grade agentic AI solutions.
• • Accelerate enterprise automation, developer productivity, operational efficiency, and time-to-market through reusable AI frameworks and modern engineering practices.
• • Drive measurable business value through AI-first platforms aligned to customer success, care, entrepreneurial ownership, and engineering excellence.

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