GenAI Solutions Architect

Overview

We're looking for a GenAI Solutions Architect to join our growing AI delivery team. You'll design and build large language model (LLM) systems that move beyond experimentation and into real-world production-powering search, summarization, knowledge assistants, and automation for enterprise clients.

This is a hands-on, execution-focused role. You'll work closely with product managers, engineers, and AI specialists to ship scalable solutions. You won't be buried in research or building theoretical models-you'll be deploying actual systems that users rely on every day.

What You'll Do

• Architect end-to-end GenAI systems, including prompt chaining, memory strategies, token budgeting, and embedding pipelines
• Design and optimize RAG (Retrieval-Augmented Generation) workflows using tools like LangChain, LlamaIndex, and vector databases (FAISS, Pinecone, Qdrant)
• Evaluate tradeoffs between zero-shot prompting, fine-tuning, LoRA / QLoRA, and hybrid approaches, aligning solutions with user goals and constraints
• Integrate LLMs and APIs (OpenAI, Anthropic, Cohere, Hugging Face) into real-time products and services with latency, scalability, and observability in mind
• Collaborate with cross-functional teams -translating complex GenAI architectures into stable, maintainable features that support product delivery
• Write and review technical design documents and remain actively involved in implementation decisions
• Deploy to production with industry best practices around version control, API lifecycle management, and monitoring (e.g., hallucination detection, prompt drift)

What You'll Bring

• Proven experience building and deploying GenAI-powered applications, ideally in enterprise or regulated environments
• Deep understanding of LLMs, vector search, embeddings, and GenAI design patterns (e.g., RAG, prompt injection protection, tool use with agents)
• Proficiency in Python and fluency with frameworks and libraries like LangChain, Transformers, Hugging Face, and OpenAI SDKs
• Experience with vector databases such as FAISS, Qdrant, or Pinecone
• Familiarity with cloud infrastructure (AWS, GCP, or Azure) and core MLOps concepts (CI / CD, monitoring, containerization)
• A delivery mindset-you know how to balance speed, quality, and feasibility in fast-moving projects

Nice to Have

• Experience building multi-tenant GenAI platforms
• Exposure to enterprise-grade AI governance and security standards
• Familiarity with multi-modal architectures (e.g., text + image or audio)
• Knowledge of cost-optimization strategies for LLM inference and token usage

This Role Is Not For

• ML researchers focused on academic model development without delivery experience
• Data scientists unfamiliar with vector search, LLM prompt engineering, or system architecture
• Engineers who haven't shipped GenAI products into production environments

Benefits

Benefits & Growth Opportunities :

• Competitive salary and performance bonuses
• Comprehensive health insurance
• Professional development and certification support
• Opportunity to work on cutting-edge AI projects
• International exposure and travel opportunities
• Flexible working arrangements
• Career advancement opportunities in a rapidly growing AI company

This position offers a unique opportunity to shape the future of AI implementation while working with a talented team of professionals at the forefront of technological innovation. The successful candidate will play a crucial role in driving our company's success in delivering transformative AI solutions to our clients.