We are seeking a Lead AI Engineer to design, build and scale cutting-edge AI applications powered by large language models. In this role, you will partner with clients to deliver tailored LLM-driven solutions, architect agentic systems and drive the adoption of emerging AI technologies across enterprise environments. Responsibilities Design, implement and maintain end-to-end AI applications, including chatbots, Q&A platforms, agent workflows and other LLM-driven solutions Collaborate directly with clients to understand their needs, identify opportunities and recommend tailored AI/LLM solutions that drive business value Architect and optimize robust data pipelines, prompt strategies and datasets to ensure effective, accurate and scalable AI models Evaluate, monitor and refine AI system performance, ensure outputs are accurate, secure, scalable and compliant with industry regulations and best practices Conduct research, design experiments and perform rapid prototyping to validate technical feasibility and demonstrate the business value of AI solutions Stay current with evolving LLM technologies, frameworks, protocols (such as MCP, A2A, ACP) and methodologies, continuously improve solution quality and client outcomes Design and implement agentic systems with frameworks such as LangChain, LangGraph and Semantic Kernel, integrate with vector databases and advanced memory architectures Develop and maintain APIs and system integrations for production-grade AI applications, including enterprise system integration (CRM, ERP, databases) Deploy AI solutions at scale, consider performance, cost-efficiency, maintainability, observability and security (including guardrails and prompt injection prevention) Implement and monitor retrieval systems (keyword search, vector search, embeddings), ranking algorithms and agent evaluation frameworks Use MLOps/AIOps practices for agentic systems and ensure robust observability and monitoring of deployed solutions Clearly communicate complex technical concepts and AI strategies to both technical and non-technical stakeholders, iterate on models based on user feedback Requirements Strong proficiency in at least one modern programming language (such as Python, Java, C#, Go, etc.); experience with web frameworks like FastAPI or similar is a plus Deep understanding of the AI application development lifecycle, including production deployment, system integration and rapid UI prototyping (Streamlit, Gradio or similar) Familiarity with major LLM platforms and APIs (OpenAI, Anthropic, Amazon Bedrock, Gemini) and related frameworks (LangChain, LangGraph, LlamaIndex, Strands Agents, etc.) Knowledge of advanced AI integration patterns (e.g., RAG, agent orchestration, tool calling), retrieval systems (keyword/vector search, embeddings) and ranking algorithms Experience to deploy AI solutions at scale, with a focus on performance, cost-efficiency, maintainability, observability and security (including guardrails and prompt injection prevention) Proven ability to evaluate generative AI quality with retrieval/classification scores, LLM-based evaluation, agent evaluation metrics and A/B testing Experience with vector databases (Pinecone, Weaviate, ChromaDB, FAISS) and semantic/hybrid search Experience to design experiments, conduct A/B tests and iterate on models based on user feedback Experience with enterprise system integration (CRM, ERP, databases) and deployment to cloud AI platforms or on-premise solutions Experience with observability and monitoring tools/frameworks, and application of MLOps/AIOps practices for agentic systems Familiarity with emerging protocols (MCP, A2A, ACP) and advanced memory architectures Proven experience in AI engineering and delivery of ML-based solutions in production environments Strong problem-solving skills, attention to detail and ability to work independently and collaboratively Excellent communication, collaboration and interpersonal skills, with the ability to explain complex technical concepts to non-technical stakeholders Technologies Proficiency in at least one modern programming language (e.g., Python, Java, C#, Go, etc.) for AI development Web frameworks: FastAPI, Streamlit, Gradio, Flask, Spring Boot, ASP.NET or similar Major LLM platforms and APIs: OpenAI, Anthropic, Amazon Bedrock, Gemini Agentic frameworks: LangChain, LangGraph, Semantic Kernel, LlamaIndex, Strands Agents Data pipeline and integration tools Vector databases: Qdrant, FAISS, Chroma, Pinecone, Weaviate, ChromaDB Retrieval and ranking systems: keyword search, vector search, embeddings, ranking algorithms Cloud AI platforms: Azure OpenAI, Amazon Bedrock, GCP Vertex AI On-premise solutions: vLLM Enterprise AI platforms: AWS AgentCore, Databricks AgentBricks, Google Agents Space, Azure AI Foundry Observability and monitoring tools/frameworks MLOps/AIOps practices for agentic systems Security and guardrail tools for AI applications Protocols: MCP, A2A, ACP Advanced memory architectures
