About The Role
We're looking for a AI 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.
Requirements
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 environmentsDeep 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 SDKsExperience with vector databases such as FAISS, Qdrant, or PineconeFamiliarity 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 projectsNice to Have
Experience building multi-tenant GenAI platformsExposure to enterprise-grade AI governance and security standardsFamiliarity with multi-modal architectures (e.g., text + image or audio)Knowledge of cost-optimization strategies for LLM inference and token usageThis Role Is Not For
ML researchers focused on academic model development without delivery experienceData scientists unfamiliar with vector search, LLM prompt engineering, or system architectureEngineers who haven't shipped GenAI products into production environmentsBenefits
Benefits & Growth Opportunities :
Competitive salary and performance bonusesComprehensive health insuranceProfessional development and certification supportOpportunity to work on cutting-edge AI projectsInternational exposure and travel opportunitiesFlexible working arrangementsCareer advancement opportunities in a rapidly growing AI companyThis 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. #J-18808-Ljbffr