Job Description, Responsibilities & Requirements
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About the Position
We are seeking a Lead AI Engineer to sit at the heart of our AI agent pipeline. The consultant will design and implement the core AI/ML components powering the event data extraction agent, including the RAG-based conversational assistant (UC2) and the reporting/analytics engine (UC3). The work spans LLM orchestration on Amazon Bedrock, knowledge base design, NLP pipelines and integration testing across the full agentic workflow.
Responsibilities
- Design and build the RAG (Retrieval-Augmented Generation) pipeline for UC2 - a conversational agent surfacing approved training content in the flow of work
- Develop UC3 analytics support - reporting and insights generation from event data
- Configure and fine-tune LLM inference on Amazon Bedrock (LLM Backbone)
- Build Natural Language Processing pipelines for parsing event input from File, Text and Voice modalities
- Design knowledge base architecture (vector storage, chunking strategy, retrieval optimization)
- Lead integration testing across the AI agent and downstream CRM output
- Collaborate with a Data Analytics consultant on ETL pipelines feeding the AI models
- Support HITL (Human-in-the-Loop) validation checkpoints for AI-generated outputs
Requirements
- Expertise in Amazon Bedrock, including LLM model selection, configuration and prompt engineering
- Proficiency in RAG architecture, covering vector databases, knowledge base design, embedding models and chunking strategies
- Skills in Natural Language Processing (NLP) such as entity extraction, classification and summarization
- Background in Python as the primary development language for ML pipelines
- Knowledge of AWS Lambda and Amazon S3 for serverless ML inference, event processing and data storage of training content and knowledge base artifacts
- Capability to perform integration testing for multi-step AI agent workflows
- Familiarity with Veeva CRM data schemas
- Upper-Intermediate English language proficiency (B2)
Nice to Have
- Flexibility to use MLflow or SageMaker for experiment tracking and model versioning
- Experience with voice-to-text processing pipelines
- AWS Certified Machine Learning – Specialty
We Offer
- Remote work from Kyiv, Ukraine
- Opportunity to work with cutting-edge AI technologies
- Collaborative and innovative work environment
About the Company
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