Gen AI Architect

Role & responsibilities

• Design and implement scalable, secure, and enterprise-grade Generative AI solutions.
• Develop LLM-based applications using RAG, prompt engineering, fine-tuning, and AI agent frameworks.
• Define end-to-end AI architecture, including model selection, data pipelines, vector databases, APIs, and cloud infrastructure.
• Build and deploy AI agents and multi-agent workflows to automate complex business processes.
• Integrate GenAI solutions with enterprise applications, databases, and third-party systems.
• Establish LLMOps/MLOps practices for model deployment, monitoring, evaluation, versioning, and continuous improvement.
• Implement AI governance, data privacy, security controls, guardrails, and responsible AI practices.
• Optimize AI solutions for latency, scalability, reliability, and cost efficiency.
• Collaborate with business stakeholders, data scientists, software engineers, and product teams to translate business requirements into technical solutions.
• Lead technical design reviews, proof-of-concept development, architecture documentation, and engineering best practices.
• Evaluate emerging GenAI technologies, frameworks, and models to identify opportunities for innovation.

Preferred candidate profile

• Bachelors or master’s degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, or a related technical discipline.
• 5 to 10 years of experience in software engineering, AI/ML, data engineering, or cloud architecture, with hands-on experience in Generative AI.
• Strong programming skills in Python and experience developing production-grade AI applications.
• Expertise in LLMs, RAG, prompt engineering, embeddings, vector databases, and LLM evaluation.
• Hands-on experience with frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent technologies.
• Experience building AI agents, agentic workflows, and enterprise AI integrations.
• Proficiency in at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform.
• Strong understanding of microservices, REST APIs, distributed systems, databases, and scalable system design.
• Experience with Docker, Kubernetes, CI/CD, MLOps/LLMOps, and production monitoring.
• Knowledge of AI security, responsible AI, data governance, and enterprise compliance requirements.
• Excellent analytical, problem-solving, communication, and stakeholder-management skills.
• Demonstrated ability to lead technical initiatives, mentor engineering teams, and deliver business-focused AI solutions.