AI Engineer
Job description
Original text imported from Reed
Lynx Recruitment is partnering with a specialist data and AI consultancy to recruit an experienced AI Engineer to work on cutting-edge Generative AI and agentic AI solutions.
Our client delivers data platforms, advanced analytics and AI/Machine Learning solutions that drive tangible business outcomes. The focus is on designing, deploying and operating production-grade GenAI and agentic systems on AWS, supporting organisations across multiple industries.
Role OverviewAs an AI Engineer, you will design, develop and deploy production-ready Generative AI solutions for enterprise clients. You will be involved across the full lifecycle of GenAI products—from proof of concept through to scalable production deployment and monitoring.
Working with modern LLMs, RAG architectures and agentic frameworks, you’ll collaborate with data engineers, solution architects and business stakeholders to deliver secure, cost-effective AI solutions on AWS.
Key Responsibilities- Design and implement production GenAI applications using LLMs (e.g. Anthropic, AWS Bedrock models)
- Build and deploy RAG systems using vector databases and semantic search
- Develop agentic AI workflows using frameworks such as LangChain, LangGraph, CrewAI or similar
- Create effective prompt engineering strategies with appropriate guardrails for production systems
- Implement monitoring, evaluation and continuous improvement frameworks for GenAI applications
- Build and maintain CI/CD pipelines for testing, deployment and version control
- Work directly with client stakeholders to gather requirements, demonstrate solutions and iterate delivery
- Document architecture designs, decisions and best practices
- 1+ year hands-on experience deploying GenAI / LLM applications into production
- Experience with AWS services such as Lambda, SageMaker, Bedrock, S3, EC2 and ECS
- Strong Python development skills with modern AI/ML libraries
- Practical experience using at least one LLM API
- Solid understanding of prompt engineering, RAG architectures and vector databases
- Experience with LangChain, LangGraph, LlamaIndex or similar frameworks
- Familiarity with Git and CI/CD workflows
- Bachelor’s degree in Computer Science, Engineering or a related discipline (2.1 or above)
- Hands-on experience building agentic AI systems with planning, tool use and multi-step reasoning
- Experience fine-tuning or adapting LLMs for domain-specific use cases
- Familiarity with LLM evaluation frameworks (e.g. RAGAS, LangSmith)
- Knowledge of LLM security, hallucination mitigation and responsible AI practices
- Master’s degree in AI, Machine Learning, Data Science or a related field
Key skills
AI-extracted from the job advert
Application advice
5 AI-generated recommendations to maximise your chances.
🤖 Highlight your GenAI production deployment experience prominently as the role specifically requires 1+ years of hands-on LLM application deployment
☁️ Showcase your AWS expertise with specific services mentioned: Lambda, SageMaker, Bedrock, S3, EC2 and ECS
🔧 Emphasise experience with agentic AI frameworks like LangChain, LangGraph or CrewAI as these are core to the role
📊 Quantify your RAG system implementations: "Built 3 RAG systems processing 50k+ documents with 95% accuracy"
🎯 Demonstrate prompt engineering skills with guardrails as this is essential for production GenAI systems
Suggested CV bullets
3 bullets our AI drafted for this specific advert, mirroring its ATS keywords.
Add these 3 bullets under your most recent experience:
- •Deployed 5 production GenAI applications using AWS Bedrock and LangChain, serving 10,000+ daily users with 99.2% uptime
- •Built RAG system processing 250,000 documents with vector databases, achieving 92% accuracy in semantic search queries
- •Implemented CI/CD pipelines for 8 LLM applications, reducing deployment time from 4 hours to 15 minutes using AWS Lambda
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Letter preview — tailored to Lynx Recruitment Ltd
Dear Hiring Manager,
Lynx Recruitment's AI Engineer position perfectly aligns with my passion for production GenAI systems and AWS deployment expertise. My hands-on experience with LLM applications, RAG architectures and agentic frameworks like LangChain makes me an ideal candidate for this cutting-edge consultancy role.
My background in designing and deploying production-ready GenAI solutions, combined with strong Python development skills and AWS services knowledge, positions me to contribute immediately to your client projects across multiple industries.
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Interview questions
10 questions generated from this advert.
Technical
- ›How would you design a RAG system for a client with 100,000+ documents?
- ›Explain the differences between LangChain and LangGraph for agentic workflows
- ›How do you implement guardrails in production GenAI applications?
- ›What AWS services would you use to deploy a scalable LLM application?
- ›How would you evaluate and monitor the performance of a GenAI system in production?
Behavioural
- ›Tell me about a time you had to explain complex AI concepts to non-technical stakeholders
- ›Describe a situation where you had to iterate on an AI solution based on client feedback
- ›How do you handle working with multiple stakeholders across different teams?
- ›Tell me about a challenging GenAI project you worked on and how you overcame obstacles
- ›Describe a time when you had to balance technical excellence with business requirements
STAR answer examples
Model answers using the Situation-Task-Action-Result framework. Adapt to your own experience.
Tell me about a time you had to explain complex AI concepts to non-technical stakeholders
Describe a situation where you had to iterate on an AI solution based on client feedback