Mid level AI Engineer
Infinity Quest
2 days ago
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
Intermediate Compensation
£ 57KJob location
Tech stack
Artificial Intelligence
Computer Programming
Databases
Continuous Integration
Object-Oriented Software Development
Software Engineering
Data Logging
Large Language Models
Prompt Engineering
Generative AI
Backend
Containerization
Solid Principles
Machine Learning Operations
Microservices
Job description
- Design, develop, and deploy production-ready Generative AI capabilities, including LLM-based applications and RAG pipelines.
- Implement and optimize LLM workflows, covering prompt engineering, context management, retrieval strategies, and model evaluation.
- Build scalable backend services to support GenAI applications, ensuring reliability, performance, and maintainability.
- Integrate GenAI tooling, such as orchestration frameworks, vector databases, and embedding pipelines, into end-to-end solutions.
- Conduct assessment and optimization of GenAI systems, including latency, cost, quality, and reliability improvements.
- Rapidly prototype new GenAI features while maintaining clean architecture, testability, and long-term maintainability.
- Collaborate with platform, product, and data teams to translate business requirements into robust AI-powered solutions.
- Contribute to best practices, documentation, and design reviews for AI engineering and backend development.
Requirements
- 5+ years of overall software engineering experience, with a minimum of 2+ years hands-on experience in AI/ML or Generative AI.
- Demonstrated experience delivering actual production implementations of AI or GenAI systems (not just research or prototypes).
- Strong understanding and practical experience with:
- Generative AI tools and frameworks (e.g., LLM orchestration, vector databases, prompt/context engineering).
- RAG architectures, embeddings, and retrieval strategies.
- Solid foundation in software engineering principles, including:
- Object-Oriented Programming (OOP)
- SOLID principles
- 12-factor application design
- Proficiency in building cloud-native applications, including containerization, CI/CD, and scalable service deployment.
- Strong programming skills and ability to write clean, maintainable, and testable code., * Experience operating GenAI workloads in production, including monitoring, logging, and cost optimization.
- Familiarity with multiple LLM providers and model deployment strategies.
- Exposure to MLOps or LLMOps practices.
- Experience working in agile or fast-paced product development environments.
