AI Solution Architect – GenAI & AI Optimization

Role Overview

We are looking for an experienced AI Solution Architect with robust hands-on expertise in Python, LLMs, RAG, Agentic AI, and AI optimization. The role will focus on architecting production-grade AI solutions and improving accuracy, response speed, latency, relevance, reliability, and cost efficiency.

Key Responsibilities

· Architect end-to-end AI/GenAI and LLM-based solutions from use case through production.

· Design and optimize RAG pipelines, including chunking, embeddings, vector search, retrieval, reranking, and context engineering.

· Optimize AI solutions for model accuracy, response quality, latency, throughput, reliability, and cost.

· Diagnose performance issues and determine whether bottlenecks originate from the model, RAG pipeline, retrieval strategy, prompt/context, agent workflow, or application design.

· Evaluate and select appropriate LLMs, embedding models, reranking models, and retrieval approaches based on quality, speed, and cost trade-offs.

· Improve prompt engineering, context management, caching, model routing, inference, and token utilization.

· Reduce hallucinations, inaccurate responses, irrelevant retrieval, and output inconsistencies.

· Design Agentic AI solutions, including tool/function calling and multi-step AI workflows.





· Establish evaluation frameworks and benchmarks to measure accuracy, relevance, groundedness, hallucination, latency, and reliability.

· Perform error analysis and continuously improve production AI solutions.

· Develop prototypes and core AI components using Python and modern AI/ML frameworks.

· Guide engineering teams on AI architecture, implementation, testing, and production optimization.

Required Skills

· Strong experience in AI/ML, GenAI, or AI Solution Architecture.

· Strong hands-on expertise in Python.

· Deep understanding of LLMs, RAG, Agentic AI, and Generative AI architectures.

· Strong knowledge of embeddings, vector search, semantic/hybrid search, reranking, prompt/context engineering, and model evaluation.

· Proven experience optimizing AI applications for speed, accuracy, latency, response quality, and cost.

· Experience with technologies such as OpenAI, Claude, Llama, Hugging Face, LangChain, LlamaIndex, Semantic Kernel, or similar.

· Experience with vector databases/search platforms such as Pinecone, Weaviate, Milvus, FAISS, Azure AI Search, or equivalent.

· Understanding of fine-tuning, model adaptation, inference optimization, model routing, caching, guardrails, and hallucination reduction.

· Experience building and optimizing production-grade AI/GenAI solutions.

📌 AI Solution Architect (India)
🏢 Philodesign Technologies
📍 India