Position - Agentic AI Platform Architect & Product Lead Budget – 20-50LPA per annum (high level) Experience – 10-15 years. Location – Hyderabad Primary Skill – Communication should be top notch. Employment Type - Full-Time / Contractor
Experience 10+ years of overall software engineering experience, with 3+ years of hands-on experience in AI/ML, LLM application development, Generative AI, platform architecture, solution architecture and technical product role where you help take a technical platform, accelerator or IP to market as a commercial offering.
Role Overview We are looking for an experienced Agentic AI Platform Architect & Product Lead to support the development and evolution of the Fluid AIQ Platform and lead AI-powered application development for upcoming client projects. The ideal candidate should have strong hands-on experience building production-ready applications using LLMs, AI Agents, RAG, tool/function calling, workflow orchestration, and modern AI frameworks. This role requires someone who can work beyond simple chatbot implementations and help design agentic applications capable of reasoning, planning, using tools, interacting with enterprise systems, and operating within defined guardrails.
The candidate will work closely with the architecture and development teams, contribute to the Fluid AIQ Platform, evaluate emerging AI technologies, build POCs, and take technical and product ownership of Fluid platform components and client AI programs. Equally important, this is a product and commercialization role: the candidate will shape how Fluid is brought to market and how its components are packaged so that each commercial module can be deployed and sold on its own and then expanded as a client’s needs grow, while reporting platform health, adoption and delivery metrics to leadership.
Key Responsibilities 1. AI / Agentic AI Architecture • Design and develop scalable architectures for Agentic AI applications, and lead and mentor architects and engineers in setting engineering standards for agentic delivery. • Identify appropriate use cases for AI agents versus traditional application workflows. • Design multi-agent and single-agent architectures based on business requirements. • Define agent orchestration, tool usage, memory, state management, and workflow patterns, and capture them as reusable reference architectures, client onboarding playbooks, implementation guides and sales-enablement material. • Evaluate AI technologies and recommend the right architecture, models, frameworks, and infrastructure, applying a replaceable-execution principle that keeps platform-owned context, governance, economics and evaluation logic independent of any single model, agent framework, coding agent or cloud. 2. Agentic Application Development Develop AI applications involving: AI Agents and Multi-Agent Systems, Agent orchestration, Tool/function calling, Workflow automation, Human-in-the-Loop (HITL), RAG, Long/short-term agent memory, Planning and reasoning workflows, MCP Server / MCP Client integrations, Enterprise API and system integrations, AI-powered decision support, Autonomous and semi-autonomous workflows. 3. LLM & Generative AI • Work with leading LLM providers such as: OpenAI, Anthropic / Claude, Azure OpenAI, Google Gemini, Open-source models, Ollama / local models. • Design model-selection strategies based on cost, performance, latency, privacy, and use case. • Implement structured outputs, function calling, prompt engineering, context management, and model fallbacks. • Evaluate and benchmark different models for specific business use cases. 4. AI Frameworks & Technologies Hands-on experience with frameworks and technologies such as: LangChain, LangGraph, LlamaIndex, AI Agents / Agent SDKs, MCP, Vector databases, RAG frameworks, AI evaluation and observability tools. The candidate should be able to evaluate frameworks rather than being dependent on a single technology. 5. AI Safety, Guardrails & Reliability • Implement AI guardrails and validation mechanisms. • Design controls for hallucination, incorrect tool usage, unauthorised actions, and unsafe outputs. • Implement human approval workflows where required. • Design reliable agent workflows with error handling, retries, fallback mechanisms, and observability, supported by the release management, environments, SLAs / SLOs and support model needed to run Fluid as a product rather than a series of projects. • Consider security, privacy, compliance, and responsible AI requirements.
Required Technical Skills Core AI / GenAI Strong understanding of: Generative AI, LLMs, AI Agents / Agentic AI, RAG, Prompt Engineering, Function / Tool Calling, Embeddings, Vector Search, Context Management, AI Evaluation. Agentic AI Strong practical understanding of: Agent architecture, Agent orchestration, LangGraph or equivalent, Tool calling, Multi-agent systems, Agent state and memory, Human-in-the-loop workflows, Autonomous / semi-autonomous workflows, Agent guardrails, Agent observability. Programming Strong programming skills in at least one of: Node.js / TypeScript, Python. Additional experience with: REST APIs, Microservices, Authentication / authorization, Async processing, Event-driven architectures, Database integration. AI Frameworks Hands-on experience with one or more: LangChain, LangGraph, LlamaIndex, OpenAI SDK, Anthropic SDK, Claude Agents SDK, Other Agent SDKs. Models Experience integrating multiple LLM providers, preferably: OpenAI, Anthropic / Claude, Azure OpenAI, Gemini, Ollama / open-source LLMs. Data & RAG Experience with: PostgreSQL, Vector databases, pgvector, Pinecone / Weaviate / Qdrant / similar, Embedding models, Document processing, Semantic search, Hybrid search, Knowledge graphs & ontology design (Neo4j or similar, RDF / OWL, GraphRAG), Multi-tenant data architecture. Cloud Strong experience with at least one cloud platform: Microsoft Azure, AWS, Google Cloud.
Nice-to-Have Skills Experience with: MCP (Model Context Protocol), Claude Code, Codex / AI coding tools, GitHub Copilot, Cursor, Ollama, Open-source LLMs, Multimodal AI, Voice AI, AI workflow automation, AI observability / evaluation platforms, Fine-tuning, Model evaluation, AI security, Prompt injection protection, PII / data protection, Enterprise SaaS integrations, CRM / ERP integrations, AI FinOps / FinOps Foundation practices, Cloud marketplace listings (Azure / AWS Marketplace), Prior product management role, Productizing consulting IP into licensed software. RPA / Automation Platforms Experience replacing or complementing traditional automation/RPA solutions with AI-agent-based approaches is particularly valuable.
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