About Cornerstone: Cornerstone powers the future-ready workforce with modern, AI-driven employment solutions. Our platform enables companies to develop, manage, and engage their talent—unlocking growth and innovation across organizations of all sizes. In this role you will.. Design, develop, and deploy production-grade AI agents and multi-agent systems using modern LLM frameworks. Build and optimize Retrieval-Augmented Generation (RAG) solutions for enterprise-scale applications. Develop intelligent workflows involving planning, reasoning, memory management, tool calling, and function calling. Design and implement scalable backend services and REST APIs using Python frameworks such as FastAPI. Integrate LLMs with enterprise systems, vector databases, APIs, and external tools. Deploy AI applications using Docker, Kubernetes, and cloud platforms such as AWS, Azure, or GCP. Optimize AI systems for latency, scalability, reliability, and cost efficiency. Build evaluation pipelines to measure retrieval quality, response relevance, hallucination rates, and overall LLM performance. Implement semantic search, hybrid search, reranking, embedding pipelines, and vector database integrations. Establish observability, monitoring, logging, and tracing for AI applications using industry-standard tools. Collaborate with product managers, software engineers, data scientists, and platform teams to deliver end-to-end AI solutions. Mentor junior engineers and promote AI engineering best practices, coding standards, and architectural excellence. Stay up to date with emerging advancements in LLMs, Agentic AI, RAG, and AI infrastructure. You've got what it takes if you have... Bachelor's or master’s degree in computer science, Artificial Intelligence, Machine Learning, or a related technical field. 8+ years of overall software engineering experience. Minimum 3 years of hands-on experience building AI/Generative AI applications using Large Language Models (LLMs). Minimum 3 years of experience developing scalable backend software and distributed systems. Strong proficiency in Java and Python and modern software engineering principles. Hands-on experience with AI orchestration frameworks such as LangGraph, LangChain, LlamaIndex, etc. Experience designing and implementing production-grade RAG systems, including embedding models, semantic search, hybrid search, reranking, and vector databases. Experience with vector databases such as Milvus, Pinecone, Weaviate, OpenSearch, Elasticsearch, FAISS, or ChromaDB. Experience integrating and deploying applications using LLMs such as OpenAI GPT, Claude, Gemini, Llama, Mistral, Amazon Nova, or Azure OpenAI. Strong experience building REST APIs using FastAPI or similar Python frameworks. Experience with containerization and orchestration technologies such as Docker and Kubernetes. Experience deploying AI solutions on cloud platforms such as AWS, Azure, or GCP. Familiarity with CI/CD pipelines, Git, and modern DevOps practices. Experience with AI evaluation, prompt engineering, observability, and monitoring tools such as LangSmith, Langfuse, MLflow, OpenTelemetry, Prometheus, or Grafana. Strong understanding of software architecture, microservices, asynchronous programming, and distributed systems. Excellent problem-solving, communication, and collaboration skills, with the ability to work effectively in a cross-functional team environment. #LI-Onsite