Senior Data AI Engineer
We are seeking a Senior Data AI Engineer to design and develop AI applications that utilize large language models and advanced integration techniques.
You will work closely with clients to build solutions such as chatbots and Q&A platforms that deliver measurable business value. In this role, you will lead AI application development, manage data pipelines, and remain at the forefront of evolving LLM technologies. If you are passionate about AI engineering and enjoy collaborating with clients to address complex challenges, we encourage you to apply.
Responsibilities
- Design and implement end-to-end AI applications, including chatbots, Q&A platforms and agent workflows
- Collaborate 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 +)
- Evaluate and refine AI system performance to ensure outputs are accurate, secure, scalable and compliant with industry regulations (Will be +)
- Conduct research and rapid prototyping to validate technical feasibility and demonstrate business value of AI solutions
- Stay current with evolving LLM technologies, frameworks and methodologies to improve solutions and client outcomes
Requirements
- Proficiency in Python and experience with web frameworks such as FastAPI or similar
- Understanding of the AI application development lifecycle
- Background in rapid UI prototyping using Streamlit, Gradio or similar frameworks
- Familiarity with major LLM platforms and APIs such as OpenAI, Anthropic, Amazon Bedrock, Gemini and related frameworks like LangGraph, LlamaIndex, Strands Agents
- Knowledge of advanced AI integration patterns such as RAG and Agents
- Experience deploying AI solutions at scale with considerations for performance, cost-efficiency and maintainability
- Competency in evaluating generative AI quality using metrics such as retrieval and classification scores as well as LLM-based evaluation methods
- Background 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
- Background in experiment design, A/B testing and model iteration based on user feedback
- Understanding of retrieval systems such as keyword search, vector search, embeddings and ranking algorithms
- Familiarity with emerging protocols including MCP, A2A and ACP
- Capability to deploy to cloud AI platforms such as Azure OpenAI, Amazon Bedrock, GCP Vertex AI or on-premise solutions like vLLM
- Familiarity with enterprise AI platforms such as AWS AgentCore, Databricks AgentBricks, Google Agents Space or Azure AI Foundry
- Skills in observability and monitoring tools and frameworks
