We're looking for a Senior AI Agent Engineer to build intelligent AI-powered applications that allow users to interact with complex business data using natural language.
The primary focus of this role is developing production-ready AI agents capable of understanding business context, interpreting domain-specific language, and translating user questions into accurate SQL queries that retrieve meaningful business insights.
You'll work across the complete AI development lifecycle—from designing semantic business layers and prompt strategies to building scalable agentic AI solutions using Large Language Models (LLMs), cloud AI services, and modern orchestration frameworks.
This is a hands-on engineering role for someone who has already built AI products used by real customers and can immediately contribute to the design and delivery of enterprise-grade AI solutions.
What You'll Do
Build Intelligent AI Agents
- Design and develop AI agents that enable users to query business data using natural language.
- Build business context layers that map domain terminology to complex relational data models.
- Design semantic representations that allow AI systems to understand company-specific concepts, entities, and relationships.
- Develop conversational AI experiences that deliver reliable, explainable responses.
- Continuously improve agent accuracy through evaluation, testing, and prompt refinement.
Natural Language to SQL
- Design and implement Text-to-SQL solutions powered by Large Language Models.
- Develop prompt pipelines that transform natural language into accurate SQL queries.
- Validate generated SQL for correctness, security, and performance before execution.
- Handle ambiguous user requests through intelligent clarification strategies.
- Improve response quality by combining structured data with contextual reasoning.
AI Product Development
- Build AI-powered features including:
- Conversational assistants
- Intelligent search
- Document understanding
- Forecasting
- Recommendation systems
- Workflow automation
- Develop rapid prototypes before production deployment.
- Integrate AI services into web applications and APIs.
- Deploy scalable production AI applications.
Data & Analytics
- Work with structured and semi-structured data from relational databases and cloud data platforms.
- Collaborate with software engineers, data engineers, and analytics teams.
- Build pipelines that connect enterprise data to AI applications.
- Support AI-powered analytics and business intelligence solutions.
AI Engineering Best Practices
- Monitor model performance and application quality.
- Implement responsible AI principles.
- Reduce hallucinations through evaluation and iterative improvements.
- Apply MLOps practices throughout the AI lifecycle.
- Stay current with advances in agentic AI, LLMs, retrieval systems, and orchestration frameworks.
Required Experience
Essential
- 5+ years of professional software engineering experience, including significant experience developing AI-powered applications.
- Proven experience building production AI agents for enterprise users.
- Experience designing conversational AI systems over structured business data.
- Strong experience translating natural language into SQL using Large Language Models.
- Experience building business context or semantic layers that map user language to enterprise data.
- Experience working with complex data models containing many entities and relationships.
- Experience implementing agentic AI architectures.
- Strong understanding of prompt engineering for structured data retrieval.
- Excellent SQL skills and experience working with relational databases.
- Strong Python programming skills.
- Experience integrating Large Language Models into production applications.
- Experience evaluating and improving AI accuracy using systematic testing and iteration.
- Strong analytical thinking and problem-solving skills.
- Ability to translate ambiguous business requirements into production-ready AI solutions.
Technical Skills
Programming
AI & Machine Learning
- Large Language Models (OpenAI, Claude, Amazon Titan or equivalent)
- LangChain
- LangGraph
- Semantic Kernel or similar orchestration frameworks
- PyTorch
- TensorFlow
- scikit-learn
- Hugging Face Transformers
Cloud
Experience with AWS services including:
- Bedrock
- SageMaker
- Lambda
- Step Functions
- RDS
- S3
- Glue
Databases
Experience with relational databases including:
- SQL Server
- PostgreSQL
- MySQL
- Amazon RDS
Data & Analytics
Experience with one or more:
- Amazon QuickSight
- Amazon Q
- Power BI
AI Engineering
Good understanding of:
- RAG architectures
- Vector databases
- Embedding models
- AI evaluation frameworks
- MLOps
- Model monitoring
- CI/CD for AI applications
Preferred Experience
Candidates with the following experience will stand out:
- Building enterprise AI copilots.
- Developing Text-to-SQL applications at scale.
- Designing semantic or business context layers.
- Building AI applications over large enterprise datasets.
- Working with knowledge graphs or metadata-driven AI.
- Deploying Retrieval-Augmented Generation (RAG) solutions.
- Building embedding-based search systems.
- Experience with FastAPI, Flask or Streamlit.
- Experience integrating AI into analytics or business intelligence platforms.
- Knowledge of responsible AI, governance and model explainability.
- Experience improving AI accuracy through evaluation, benchmarking and continuous optimisation.
What Success Looks Like
Within your first few months, you'll be contributing directly to the development of AI agents capable of answering complex business questions using enterprise data.
You'll be comfortable taking a business problem, modelling the domain, designing the AI architecture, implementing the solution, and continuously improving its accuracy based on real-world usage.
This role requires someone who enjoys solving difficult technical challenges and building AI products that deliver measurable business value from day one.
