Job Description

About the Role

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.

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 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.