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