Senior AI Engineer
Primary stack
Nice to have's
Job description
About the Position
We are seeking a Senior AI Engineer to create and implement advanced AI applications, including chatbots, Q&A systems, and agent workflows.
This position requires keeping pace with advancements in LLM technologies and driving innovation in AI projects. Join us to enhance your skills in AI engineering and deliver impactful solutions tailored to client needs.
Responsibilities
- Design AI applications such as chatbots, Q&A platforms, and agent workflows
- Collaborate with clients to understand requirements, identify opportunities, and propose LLM-driven solutions
- Build data pipelines, prompt strategies, and datasets to optimize AI models
- Optimize AI system performance to ensure security, scalability, and compliance with industry standards
- Conduct research and develop prototypes to validate feasibility and prove business value
- Evaluate LLM advancements, frameworks, and methodologies to improve solutions for client needs
Requirements
- 3+ years of experience in Python, with web frameworks like FastAPI or similar
- Understanding of AI application development lifecycle
- Background in rapid UI prototyping using tools like Streamlit or Gradio
- Familiarity with major LLM platforms and APIs like OpenAI, Anthropic, Amazon Bedrock, and Gemini, alongside related frameworks such as LangGraph, LlamaIndex
- Knowledge of integration techniques like RAG and Agents
- Proficiency in deploying AI solutions at scale with attention to performance and maintainability
- Skills in evaluating generative AI quality with metrics like retrieval and classification scores
- Expertise in AI engineering and delivering machine learning solutions
- Competency in problem-solving with a strong attention to detail
- Clear communication and collaboration abilities with demonstrated interpersonal skills
Nice to Have
- Expertise in experiment design and utilizing A/B tests to refine models based on feedback
- Understanding of retrieval systems like keyword search, vector search, and embeddings, along with ranking algorithms
- Familiarity with emerging protocols like MCP, A2A, and ACP
- Capability to deploy to platforms like Azure OpenAI, Amazon Bedrock, GCP Vertex AI, or on-premise setups like vLLM
- Background in enterprise AI solutions including AWS AgentCore, Databricks AgentBricks, Google Agents Space, or Azure AI Foundry
- Proficiency in using observability and monitoring tools effectively
Technologies
- Python, PyTorch, Hugging Face, LangChain
- Vector databases including Qdrant, FAISS, Chroma
- APIs for LLMs such as Azure OpenAI and AWS Bedrock
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