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We are seeking a Lead AI Engineer to spearhead the design and development of cutting-edge AI solutions, leveraging the latest advancements in generative AI and LLM technologies to deliver seamless and scalable applications for our clients.
Responsibilities
- Design, implement and maintain end-to-end AI applications, including chatbots, Q&A platforms and agent workflows
- Collaborate directly with clients to understand their needs, identify opportunities and recommend LLM-driven solutions
- Develop and manage robust data pipelines, prompt strategies and datasets to ensure effective and accurate AI models (Will be a plus)
- Evaluate and refine AI system performance, ensuring outputs are accurate, secure, scalable and compliant with industry regulations (Will be a plus)
- Conduct research and rapid prototyping to validate technical feasibility and demonstrate the business value of AI solutions
- Stay current with evolving LLM technologies, frameworks and methodologies to continuously improve solutions and client outcomes
Requirements
- Strong proficiency in Python, experience with web frameworks like FastAPI or similar
- Understanding of the AI application development lifecycle
- Experience with rapid UI prototyping using Streamlit, Gradio or similar frameworks
- Familiarity with major LLM platforms and APIs (OpenAI, Anthropic, Amazon Bedrock, Gemini) and related frameworks (LangGraph, LlamaIndex, Strands Agents, etc.)
- Knowledge of advanced AI integration patterns (e.g., RAG, Agents)
- Experience deploying AI solutions at scale, with considerations for performance, cost-efficiency and maintainability
- Proven ability to evaluate generative AI quality using metrics such as retrieval and classification scores, as well as LLM-based evaluation methods
- Proven experience in AI engineering and delivering ML-based solutions
- Strong problem-solving skills and attention to detail
- Excellent communication, collaboration and interpersonal skills
Nice to have
- Experience designing experiments, conducting A/B tests and iterating on models based on user feedback
- Understanding of retrieval systems (keyword search, vector search, embeddings) and ranking algorithms
- Familiarity with emerging protocols such as MCP, A2A, ACP, etc.
- Experience deploying to cloud AI platforms (Azure OpenAI, Amazon Bedrock, GCP Vertex AI) or on-premise solutions (e.g., vLLM)
- Experience with enterprise AI platforms such as AWS AgentCore, Databricks AgentBricks or Google Agents Space or Azure AI Foundry
- Experience with observability and monitoring tools and frameworks