Lead AI Engineer

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