AI Solution Architect

We are looking for a Hands-On AI Solution Architect who lives and breathes technology. This is a 100% Individual Contributor (IC) role; there is zero people management, headcount handling, or administrative oversight. You will sit directly across the table from enterprise clients to solve complex technical problems, architect scalable GenAI systems, and justify technology trade-offs in real time. If you are a senior technologist who still loves rolling up your sleeves, writing code, optimising pipelines, and building state-of-the-art AI applications, this role is built for you.

Responsibilities:

• Client Technical Leadership: Interface directly with client engineering and executive teams to comprehend requirements, evaluate architectural options, and articulate technology trade-offs (e. g., fine-tuning vs. RAG, framework selections).
• End-to-End GenAI System Design: Architect and implement production-grade agentic AI workflows, retrieval-augmented generation (RAG) pipelines, and multi-agent systems using frameworks like LangChain and LangGraph.
• Infrastructure and Infrastructure Operations: Handle real-time technical scenarios including GPU scaling strategy, embedding model selection, vector indexing, fine-tuning execution, and model unlearning.
• Enterprise Integration: Define microservices patterns, REST/GraphQL APIs, and event-driven architectures to integrate AI capabilities into enterprise applications.
• Security and Governance: Ensure data security, privacy, encryption, IAM/RBAC standards, and Responsible AI practices across cloud deployments.
• Hands-on Collaboration: Work directly alongside Data Science, MLOps, and core software engineering teams to turn concepts into reliable production systems.

Requirements:

• Overall Experience: Minimum 10+ years of total software engineering and system architecture experience.
• AI/ML and GenAI Depth: 3-4+ years of recent, hands-on engineering experience designing and deploying GenAI architectures, LLM orchestration, and RAG pipelines. Prior deep ML engineering background (PyTorch, TensorFlow) is highly desirable.
• Frameworks and Tooling: Deep expertise with LangChain, LangGraph, Hugging Face Transformers, and agentic design patterns.
• Vector Databases and Data Stores: Hands-on experience with vector stores (Pinecone, Weaviate, Qdrant, Milvus) alongside relational and NoSQL databases (PostgreSQL, MongoDB).
• Cloud Platforms: Strong architectural proficiency in AWS (Bedrock, SageMaker, Lambda), Azure (OpenAI Service, AKS), or GCP (Vertex AI, BigQuery). Cloud certifications (AWS/Azure/GCP Architect) are a strong plus.
• System Design: Microservices architecture, API gateways, WebSockets for real-time applications, serverless computing, and event-driven systems.

Good to Have:

• Technological Hunger: You are passionate about staying on the bleeding edge of AI/ML, experimenting with new models, and tackling technical bottlenecks yourself.
• Architectural Clarity: You don't just use tools; you can explain the exact why behind choosing a specific framework, vector database, or deployment strategy.
• Zero Management Desire: You are explicitly looking to stay on a technical IC track and have no desire to transition into people management or project coordination.