AI Technical Lead
Cpl Group
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Job summary
Cpl Group in Dublin is seeking an AI Technical Lead to design, implement, and scale AI solutions across enterprise systems. You will guide cross-functional teams in adopting responsible AI while delivering high-value automation and improved user experiences.
The role focuses on LLMs, RAG, and agentic AI across financial services apps, with hybrid Dublin work arrangements and a day-rate contract.
Qualifications
- 5+ years of hands-on ML/AI experience.
- Experience implementing production LLM applications.
- Design and develop RAG systems with effective vector retrieval.
- Agentic AI workflows with tool-using capabilities.
- Strong prompt engineering knowledge & best practices.
- Experience with LangChain, LangGraph, Llama Index, Semantic Kernel.
Responsibilities
- Design comprehensive AI architectures with LLMs, vector databases, and agentic frameworks.
- Establish patterns for AI integration with existing enterprise applications.
- Develop robust RAG implementations for knowledge-based processes.
- Balance innovation with delivering measurable business value.
Skills
Education
Tools
Job description
Dublin Hybrid - €600-€650 per day
We are seeking an experienced AI Technical Lead to drive the implementation of advanced AI capabilities supporting our enterprise transformation programs. The ideal candidate will have deep expertise in modern AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI systems. This role will focus on leveraging cutting-edge AI frameworks to deliver business value through intelligent automation, enhanced user experiences, and optimized workflows in our financial services applications.
As an AI Technical Lead, you will be responsible for designing, implementing, and scaling AI solutions across our enterprise systems. You will work at the intersection of emerging AI technologies and practical business applications, guiding cross-functional teams in the adoption of responsible AI practices while ensuring technical excellence and innovation.
- Design comprehensive AI architectures incorporating LLMs, vector databases, and agentic frameworks
- Establish patterns for effective AI integration with existing enterprise applications
- Develop robust RAG implementations for knowledge-intensive business processes
- Balance innovation with practical delivery of strategically agreed capabilities to ensure measurable business value
- Lead the evaluation and selection of appropriate AI models, tools, and frameworks
- Design end-to-end AI workflows incorporating prompt engineering, context handling, and tool use
- Guide the responsible implementation of generative AI capabilities in businesscritical systems, project SDLC and system workflows
- Establish best practices for AI development, testing, and deployment
- Develop LLM-powered applications with sophisticated reasoning capabilities
- Create RAG systems with effective vector retrieval, context management, and response generation
- Build agentic AI workflows that can perform multi-step tasks with appropriate tool use
- Implement AI-powered document intelligence and information extraction systems
- Design hybrid AI architectures combining symbolic and neural approaches where appropriate
- Develop generative AI solutions for intuitive UI creation and personalized user experiences
- Implement fine-tuning and model adaptation strategies for specialized domains
- Develop robust evaluation frameworks for AI system performance and reliability
- Create effective prompt engineering strategies for consistent and controlled AI outputs
- Design and implement AI guardrails and safety mechanisms for enterprise applications
- Mentor development teams in advanced AI implementation techniques
- Collaborate with data scientists, MLOps engineers, and business analysts
- Evangelize AI best practices and responsible AI principles across the organization
- Provide technical oversight for internal and vendor-provided AI solutions
- 5+ years of hands-on experience with ML/AI technologies
- Demonstrated experience implementing production LLM applications
- Proven ability to design and develop RAG systems with effective vector retrieval
- Experience designing agentic AI workflows with tool-using capabilities
- Working knowledge of prompt engineering techniques and best practices
- Hands-on experience with LLM frameworks (LangChain, LangGraph, Llama Index, Semantic Kernel, etc.)
- Experience in full stack development is a plus, should be able to work with UI engineers and API leads to build UIs including conversation AI usecases
- Understanding of model fine-tuning, quantization, and optimization approaches
- Strong software engineering background with proficiency in Python, JavaScript/TypeScript
- Experience with vector databases (Pinecone, Weaviate, Qdrant, etc.)
- Knowledge of modern AI development tools (GitHub Copilot, Claude/GPT APIs, Hugging Face)
- Experience with containerization and orchestration (Docker, Kubernetes)
- Understanding of MLOps practices for AI application deployment
- Experience integrating AI capabilities into enterprise systems
- Knowledge of API development and microservices architecture
- Cloud platform experience (AWS)
- Understanding of event-driven architectures and messaging patterns
- Experience with observability and monitoring for AI systems
- Proven track record leading technical teams on innovative projects
- Excellent communication with technical and non-technical stakeholders
- Ability to translate business requirements into AI-enabled solutions
- Experience working in Agile/Scrum environments
- Problem-solving skills and innovative thinking
- Bachelor's or Master's degree in Computer Science, AI, or related field
- Relevant certifications in AI/ML beneficial
- Experience leading enterprise-scale AI implementation projects
- Demonstrated commitment to continuous learning in the rapidly evolving AI landscape
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