02 Key Responsibilities

- 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 rapid-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

03 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

04 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 LangChain, LlamaIndex, 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

05 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

06 Education Qualification
- B.E. / B.Tech in Computer Science, IT, Electronics, or related engineering discipline Preferred
- BCA / MCA / B.Sc. (Computer Science) Preferred

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 AI Solutions Architect (Ahmedabad)
🏢 Binary Web Solutions
📍 Ahmedabad