Lead AI Engineer

Description

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