Lead AI Engineer
We are looking for a skilled and innovation-focused Lead AI Engineer to lead the creation of advanced generative AI solutions.
If you are passionate about artificial intelligence and experienced in building systems that drive tangible business outcomes, this role is for you. Join a dynamic team and develop AI-driven technologies that solve real-world problems in a collaborative and future-oriented setting.
Responsibilities
- Design and maintain AI applications such as chatbots, Q&A platforms, and agent workflows
- Collaborate with clients to understand needs, identify opportunities, and propose LLM-powered solutions
- Build and optimize data pipelines, prompt strategies, and datasets for reliable and effective AI models
- Conduct research and prototyping to validate technical feasibility and demonstrate AI solutions' business value
- Optimize AI system performance for accuracy, security, scalability, and industry compliance
- Stay informed about advancements in LLM technologies, frameworks, and methodologies to enhance outcomes
Requirements
- 5+ years of experience in Python, with web frameworks like FastAPI or similar
- 1+ years of leadership experience
- Background in AI application development lifecycle
- Skills in rapid UI prototyping using Streamlit, Gradio, or similar frameworks
- Familiarity with major LLM platforms and APIs (OpenAI, Anthropic, Amazon Bedrock, Gemini) and related frameworks (e.g., LangGraph, LlamaIndex)
- Knowledge of advanced AI integration patterns (e.g., RAG, Agents)
- Proficiency in deploying scalable AI solutions with cost and performance considerations
- Proven ability to assess generative AI quality using retrieval/classification scores and LLM-based evaluation methods
- Expertise in AI engineering and implementing ML-driven solutions
- Competency in problem-solving with strong attention to detail
- Effective communication, collaboration, and interpersonal skills
Nice to have
- Skills in designing experiments and conducting A/B tests with iterative model improvements
- Understanding of retrieval systems (e.g., keyword search, vector search, embeddings) and ranking algorithms
- Knowledge of emerging protocols such as MCP, A2A, and ACP
- Proficiency in deploying cloud AI platforms (Azure OpenAI, Amazon Bedrock, GCP Vertex AI) or on-premise solutions (e.g., vLLM)
- Background in enterprise AI platforms such as AWS AgentCore, Databricks AgentBricks, Google Agents Space, or Azure AI Foundry
- Familiarity with observability and monitoring tools or frameworks
Technologies
- Python, PyTorch, Hugging Face, LangChain
- Vector databases including Qdrant, FAISS, Chroma
- APIs for LLMs such as Azure OpenAI and AWS Bedrock
