AI Solutions Architect

​ Key Responsibilities Client Engagement & Solution Design ▸ Engage directly with clients to understand business problems, operational bottlenecks, and automation opportunities ▸ Translate business requirements into detailed AI architecture blueprints — covering model selection, infrastructure, data flow, and integration points ▸ Recommend cloud vs.

on-premise vs. hybrid AI deployment based on client security posture, data sensitivity, and budget ▸ Present AI solution proposals to both technical and non-technical stakeholders with clarity AI Implementation & Development ▸ Design and deploy AI automation workflows using n8n, Make, and Zapier integrated with LLM capabilities ▸ Integrate OpenAI, Anthropic Claude, Google Gemini, and other LLM APIs into client systems and internal products ▸ Set up and configure local AI deployments using Ollama, LM Studio, or similar frameworks on client infrastructure ▸ Build and maintain RAG (Retrieval-Augmented Generation) pipelines with vector databases (Pinecone, Weaviate, Qdrant, ChromaDB) ▸ Develop AI Agents and AI Assistants tailored to specific business use cases — customer support, sales enablement, operations, inventory ▸ Build and deploy AI chatbots integrated into websites, WhatsApp, Shopify storefronts, and internal tools ▸ Configure and manage MCP (Model Context Protocol) servers for context-aware AI deployments E-Commerce & Platform AI ▸ Implement AI-powered features within Shopify ecosystems — smart recommendations, AI search, automated customer interactions ▸ Integrate AI workflows into Zoho Flow, Zoho CRM, and other business platforms used by SME and enterprise clients ▸ Build AI-assisted ERP/SAP integration layers for data transformation and decision support Infrastructure & Security ▸ Set up GPU/AI server infrastructure (NVIDIA-based or cloud GPU instances) for local model deployment ▸ Containerize AI services using Docker; manage deployment in basic server environments ▸ Enforce AI security best practices — data privacy, prompt injection defense, model access control, audit logging ▸ Evaluate and implement on-premise AI deployments for clients with data residency or compliance requirements Continuous Improvement ▸ Stay current on fast-moving AI tooling, models, and frameworks — bring new ideas into the practice proactively ▸ Document architectures, deployment playbooks, and integration guides for internal knowledge sharing ▸ Mentor junior team members on AI implementation practices Required Technical Skills ▸ LLM Integration: Hands-on experience integrating OpenAI GPT, Anthropic Claude, or Google Gemini APIs into production systems ▸ Local AI Deployment: Practical experience with Ollama, LM Studio, or equivalent for running open-weight models locally ▸ RAG & Vector Databases: Ability to build end-to-end RAG pipelines; working knowledge of at least one vector DB ▸ AI Agents & Assistants: Built at least one functional AI agent or assistant — tool-using, multi-step, or autonomous ▸ Automation Platforms: Hands-on experience with n8n, Make (Integromat), or Zapier for workflow automation ▸ API Integrations: Comfortable working with REST APIs, webhooks, and third-party integration patterns ▸ Prompt Engineering: Practical understanding of system prompts, few-shot prompting, chain-of-thought, and output structuring ▸ Programming: Working knowledge of Python or Node.

js — enough to build integrations, scripts, and lightweight services ▸ Docker & Deployment: Ability to containerize AI services and deploy to Linux-based server environments ▸ AI Security Basics: Awareness of data privacy in AI systems, prompt injection risks, and secure API key management Requirements Preferred Skills ▸ Experience with MCP (Model Context Protocol) server setup and configuration ▸ Shopify app development or AI integrations within the Shopify ecosystem ▸ Zoho Flow / Zoho One automation experience ▸ Familiarity with ERP/SAP data structures and integration patterns ▸ Knowledge of GPU infrastructure — NVIDIA CUDA, cloud GPU provisioning (AWS, GCP, Azure) ▸ Experience with Lang.

Chain, Llama. Index, or similar orchestration frameworks ▸ Working knowledge of embedding models and semantic search ▸ WhatsApp Business API integrations ▸ Basic understanding of fine-tuning or model customization workflows ▸ Exposure to enterprise AI governance and compliance frameworks Experience Requirements ▸ 3 to 5 years of total professional experience in technology roles ▸ Minimum 1.

5 to 2 years of direct, hands-on AI/ML implementation experience (not just exposure) ▸ At least 2 to 3 end-to-end AI projects delivered — from problem definition to deployment ▸ Prior experience in a digital agency, product company, or consulting environment is strongly preferred ▸ Client-facing experience or technical pre-sales exposure is a significant plus ▸ Experience in e-commerce, retail, or B2B SaaS verticals preferred Note: We value demonstrated project work over credentials.

A strong portfolio of real-world AI implementations will outweigh years of experience in our evaluation.

Education

Qualification ▸ B.E. / B.Tech in Computer Science, IT, Electronics, or related engineering discipline — Preferred ▸ BCA / MCA / B.Sc. (Computer Science) with strong practical AI experience — Acceptable ▸ No formal degree with exceptional portfolio and demonstrated AI implementation experience — We will consider ▸ Relevant certifications (Deep.

Learning.AI, Google Cloud AI, AWS AI/ML, Microsoft Azure AI) are a bonus, not a requirement Benefits Why Join Binary You'll work on real problems, not demos. Our clients are live businesses — D2C brands, enterprise operations teams, retail chains — with real data, real pressure, and real budgets. Your work ships and impacts their business.

You'll own your domain. This isn't a support role. You'll be the AI Architect — with the authority and responsibility to make meaningful technical decisions. You'll stay ahead of the curve. Binary invests in tool licenses, GPU access, and learning budgets. You'll experiment with the latest models and frameworks, not just maintain legacy integrations.

Privacy-first AI is our differentiation. We believe in giving clients the choice between cloud and on-premise AI. You'll build solutions that respect data sovereignty — a growing need in India's enterprise market. You'll grow with the practice, not just in it. AI is Binary's fastest-growing vertical. The person in this role today shapes what the team looks like in two years.

Growth Opportunity Timeline Role & Scope Now AI Solutions Architect — Design and implement AI systems across client engagements 12–18 Months Senior AI Solutions Architect — Larger clients, more complex architectures, team influence 24–36 Months AI Practice Lead / Head of AI Solutions — Own the practice, build the team, shape Binary's AI product IP Company Culture ▸ Builders over buzzword followers — We respect people who ship, not people who theorize ▸ Experimentation is encouraged — We'd rather you try something new and learn than play it safe every time ▸ Clients are partners — We don't disappear after delivery; we build long-term relationships ▸ Continuous learning is the norm — The team shares releases, experiments, and learnings regularly ▸ Flat, direct communication — No layers of bureaucracy; you can reach founders and senior leadership with a message ▸ Outcomes over optics — We measure by what got built and what it achieved, not by meeting attendance or long hours