AI Engineer

You will be working with Large Language Models (LLMs), both open-source (like LLaMA, Mistral, GPT-NeoX) and API-based (like OpenAI, Anthropic). Your responsibilities will include developing and managing multi-agent system architectures for AI-driven applications, conducting comparative evaluation of LLMs based on quality, speed, cost, and reliability, designing, testing, and optimizing prompts, context management strategies, and resolving common issues like hallucinations and irrelevant outputs. You should understand the basics of model fine-tuning and customizing pre-trained models for domain-specific use-cases. Additionally, you will be building and integrating backend systems using Node.js, RESTful/GraphQL APIs, and database management (SQL / NoSQL), deploying applications on cloud platforms like Firebase, AWS, or Azure, and managing resources effectively. You will implement and manage agentic AI systems, A2A workflows (agent-to-agent), and RAG (Retrieval-Augmented Generation) pipelines, handle hosting, scaling, and deployment of websites/web apps, and maintain performance during high traffic loads. Optimization of infrastructure for cost-effectiveness and high availability will be one of your key responsibilities. You will also work with frameworks like LangChain, AutoGen, or equivalent LLM orchestration tools, lead or collaborate on AI-powered product development from concept to deployment, and balance between rapid prototyping and building production-grade, reliable AI applications. Qualifications Required: - 2-3 years of experience (or equivalent project experience) - Strong understanding of LLM evaluation frameworks and metrics - Familiarity with LangChain, AutoGen, Haystack, or similar AI agent management libraries - Working knowledge of AI deployment best practices - Basic knowledge of Docker, Kubernetes, and scalable hosting setups Preferred Skills: - Experience in managing cross-functional teams or AI development interns is a plus Bonus Advantage: - Prior experience working on AI-powered SaaS platforms - Contribution to open-source AI projects or AI hackathons - Familiarity with data privacy, security compliance, and cost management in AI applications,