Applied AI Solution Engineer - Kearney Activate
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 like LangGraph or LangChain.
- Participate in client conversations to capture requirements and translate business needs into technical solution components.
- Diagnose and help resolve technical issues; escalate 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.