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
* Observability and monitoring tools/frameworks
* MLOps/AIOps practices for agentic systems
* Security and guardrail tools for AI applications
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