Applied AI Solution Engineer - Kearney Activate in London

Explicitly calls out "vibe coding" — expects fast prototyping and AI-assisted, improvisational development skills.

About the Role

Junior Applied AI Solution Engineer working with cross‑functional teams to design, build, and deploy AI‑driven applications that leverage LLMs, agents, and retrieval techniques. The role is hands‑on and client‑facing, focused on delivering end‑to‑end AI solutions and supporting adoption and implementation efforts.

Job Description

Role: Junior Applied AI Solution Engineer working within cross‑functional teams to design, build, and deploy AI‑driven applications that leverage large language models (LLMs), retrieval‑augmented generation (RAG), and autonomous agents. This is a hands‑on, client‑facing engineering role contributing to solution design, development, testing, and delivery under senior engineer guidance.

Key Responsibilities:

• Assist in building AI‑powered applications, particularly those using LLMs, RAG, and agents.
• Support end‑to‑end implementation of AI components into microservices, APIs, and client‑facing applications.
• Contribute to agent and workflow development, including tool‑calling, memory, and routing logic using frameworks such as LangGraph or LangChain.
• Participate in client conversations to capture requirements and translate business needs into technical solution components.
• Diagnose and help resolve technical issues; elevate complex blockers to senior engineers or R&D teams.
• Document learnings, reusable patterns, and contribute to team knowledge sharing.
• Continuously learn new models, tools, frameworks, and experiment with open‑source models and orchestration frameworks.

Requirements:

• Hands‑on experience or strong foundational knowledge in Python, SQL, APIs, and basic data pipelines.
• Experience building small‑scale microservices or automation scripts.
• Familiarity with LLM APIs such as Azure OpenAI, Gemini, Hugging Face, or Anthropic.
• Understanding of GenAI concepts: prompting, embeddings, fine‑tuning, vector search, and RAG development.
• Basic understanding of model fine‑tuning, vector stores, and embedding generation.
• Strong analytical and problem‑solving mindset and willingness to learn new technologies.
• Comfortable communicating with non‑technical audiences and translating business needs into technical requirements.

Nice to Have:

• Experience building data pipelines, preparing and indexing datasets, and implementing embedding generation and retrieval evaluation.
• Exposure to MLOps practices, Docker, CI/CD, or cloud platforms (AWS/GCP/Azure).
• Hands‑on work with agent workflows or multi‑step reasoning models.
• Experience with fast prototyping and AI‑assisted development ("vibe coding").

Working Context:

Solutions are primarily built using existing foundational models (closed and open‑source), sometimes with light fine‑tuning; training foundational models from scratch is not required. Client‑facing consulting environment with growth and learning opportunities through global programs and partner certifications.

Skills:

• Solution Design
• Client‑facing Communication
• Problem Solving
• Analytical Thinking
• Cross‑functional Collaboration
• Documentation
• Requirements Translation
• Continuous Learning
• Adaptability
• Ethical AI Implementation
• Prototyping

Experience Level: Junior

Employment Type: Full‑time

Benefits:

• Global learning programs
• Professional development and growth
• Collaborative ecosystem with technology partners