Lead AI Engineer

Job 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