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Role Overview
The AI Engineer is an early-career role for engineers who are passionate about building with modern AI and ready to grow into the next generation of AI specialists. You will work alongside senior and principal engineers to build, ship, and improveproductionAI features, learning the craft of applied AI engineering on real systems that serve real users.
This role is hands-on from day one. You will write code, prototype ideas, evaluate models, integrate APIs, and contribute to the platform that powers AI across the organization. You will be supported with strong mentorship, clear scope, and the room to grow into more senior responsibilities over time.
Key Responsibilities
Build and Ship
Implement features inproductionAI applications, including LLM integrations, prompt workflows, retrieval pipelines, and supporting backend services.
Develop andmaintaincomponents of RAG systems, including data ingestion, chunking, embedding generation, and retrieval logic.
Write clean, tested, well-documented Python code that meets team standards for quality and maintainability.
Build internal tools, scripts, and prototypes that accelerate the team's ability to experiment and iterate.
Evaluate and Improve
Run experiments to evaluate model performance, prompt variations, retrieval strategies, and end-to-end system behavior.
Develop andmaintainevaluationdatasets, test cases, and regression checks for AI features.
Analyze production logs and metrics toidentifyquality issues, latency bottlenecks, and cost optimization opportunities.
Contribute toincident response and root-cause analysis for AI system issues.
Learn and Contribute
Stay current with the AI ecosystem by following research, exploring new tools, and bringing useful ideas back to the team.
Participate actively in code reviews, design discussions, and team rituals, asking questions and offering perspectives.
Document your work clearly so that teammates can build on it and learn from it.
Pair with senior engineers on complex problems and gradually take on larger scope as you grow.
Collaborate
Work closely with product managers, designers, and other engineers to understand requirements and ship features that solve real user problems.
Communicate progress, blockers, and trade-offs clearly in standups, written updates, and design documents.
Support other teams by answering questions about AI capabilities and limitations.
Required Qualifications
1 to 2 years of professional software engineering experience (internships, co-ops, and substantialopen sourcecontributions count).
Strong programming skills in Python, with familiarity in writing modular, testable code.
Working knowledge of how large language models behave in practice, including experience calling LLM APIs (OpenAI, Anthropic, or open-weight models) in at least one project.
Familiarity with at least one of the following: RAG systems, prompt engineering, vector databases, embeddings, or basic agent patterns.
Solid foundation in software engineering basics including Git, REST APIs, JSON, SQL, and at least one cloud environment.
Strong written and verbal communication skills with a willingness to ask questions and engage in technical discussion.
Bachelor's degree in Computer Science, Data Science, Machine Learning, Engineering, or a related field, or equivalent demonstrable experience.
Preferred Qualifications
Experience with at least one AI framework such asLangChain,LlamaIndex, Hugging Face Transformers, orDSPy.
Exposure to vector databases (Pinecone,Weaviate,pgvector, Vertex AI Vector Search) and embedding models.
Familiarity with one major cloud platform (Azure, or AWS), particularly the managed AI services.
Comfort with Docker, basic CI/CD workflows, and modern engineering practices.
A portfolio of personal projects,open sourcecontributions, hackathon work, or coursework thatdemonstratescuriosity and initiative in AI.
Experience with web frameworks (FastAPI, Flask) or frontend basics (React, TypeScript) is a plus but notrequired.
Coursework or self-directed learning in machine learning, deep learning, NLP, or information retrieval.
Technical Skill Profile
Core Programming: Python (required), familiarity with JavaScript or TypeScript helpful.
AI Tooling: Comfort calling LLM APIs, basic prompt engineering, familiarity with at least one framework (LangChain,LlamaIndex, Hugging Face).
Data and Storage: SQL, JSON, basic familiarity with vector databases and traditional databases.
Cloud and Engineering: Git, REST APIs, basic Docker, at least one cloud environment (Azure, or AWS).
Bonus: Notebook environments (Jupyter,Colab), evaluation tools (RAGAS,LangSmith, Weights and Biases), basic frontend development.
Competencies We Value
Curiosity: Genuine interest in how AI works and a habit of digging into details rather than treating models as black boxes.
Ownership: Willingness to see problems through, even when the path is unclear.
Communication: Comfort asking questions, sharing progress, and explaining your thinking.
Quality Mindset: Pride in writing code that is clear, tested, and easy for others to work with.
Learning Velocity:A track recordof picking up new tools, languages, and concepts quickly.
Collaboration: Generosity with teammates, openness to feedback, and willingness to help others succeed.
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