GenAI Architect: Build LLM-Powered, Scalable AI Systems

Role Overview

We are looking for an experienced and visionary GenAI Specialist to design, build, and deploy next-generation AI-powered systems using Large Language Models (LLMs) and advanced machine learning techniques.

The role requires strong technical depth in GenAI, production-grade ML systems, and the ability to translate business needs into scalable AI solutions that drive operational efficiency and innovation.

You will work closely with cross-functional teams to build intelligent systems powered by LLMs, agentic workflows, and modern AI infrastructure.

• Design and build agentic AI systems and implement MCP (Model Context Protocol)-based architectures
• Develop and deploy LLM-powered applications using RAG, Graph RAG, and tool-using agents
• Build and maintain scalable microservices-based AI systems
• Develop ML solutions for fraud detection, compliance, and financial crime prevention
• Improve model performance by reducing false positives and increasing detection accuracy
• Design and deploy synthetic data generation pipelines using Generative AI
• Process large-scale transactional data (millions of events daily) using ML systems
• Conduct A/B testing for ML-driven systems and optimize business KPIs
• Perform hyperparameter tuning and model optimization for precision/recall improvement
• Collaborate with stakeholders to translate business requirements into AI solutions
• Write clean, scalable, production-grade Python code (PEP8 standards)

• Strong hands-on experience in GenAI / LLM-based systems
• Expertise in RAG, Agents, Graph RAG, and LLM orchestration frameworks
• Experience with open-source and closed-source LLMs (LLaMA, Mistral, Gemini, GPT, Claude, etc.)
• Strong Python skills with focus on NLP and production ML systems
• Experience in Prompt Engineering, fine-tuning (LoRA, QLoRA, PEFT)
• Knowledge of transformers, GANs, VAEs, and other generative models
• Strong understanding of NLP tasks (classification, QA, summarization, NER, etc.)
• Experience in ML model deployment and production systems
• Good understanding of APIs, Docker, and cloud environments
• Familiarity with databases such as Postgre
  
  SQL, vector DBs (e.g., Qdrant) is a plus

• Experience with financial services / fraud / risk modeling
• Exposure to MLOps / LLMOps pipelines
• Knowledge of Kubernetes or distributed systems
• Experience with real-time data processing systems

• Strong analytical and problem-solving mindset
• Ability to design scalable long-term AI strategies
• Clear communication with technical and non-technical stakeholders
• Ownership mindset and ability to work in fast-paced environments