Junior AI Developer

About the Role

Accuris is seeking a curious and technically motivated Junior AI Developer to contribute to the development of AI-powered applications and intelligent product capabilities. This role involves working with experienced developers, AI/ML specialists, and product teams to build, test, and integrate AI-driven solutions. You will gain hands-on exposure to machine learning concepts, large language models (LLMs), data processing, APIs, and AI application development.

Key Responsibilities

AI Application Development

• Assist in designing, developing, testing, and maintaining AI-powered applications and product features.
• Support the integration of AI and machine learning capabilities into existing applications and workflows.
• Work with APIs and AI frameworks to build application features using machine learning models or large language models.
• Assist in developing and maintaining prototypes, proof-of-concepts, and production-ready AI features.
• Write clean, modular, maintainable code and follow established engineering practices.

Data Processing and Model Integration

• Assist in preparing, cleaning, transforming, and validating data for AI-related applications.
• Support the implementation and evaluation of AI/ML models and model-powered services.
• Experiment with prompts, model configurations, and retrieval techniques to improve AI-generated outputs.
• Assist with retrieval-augmented generation (RAG), embeddings, vector search, or document-processing workflows.
• Help evaluate AI outputs for accuracy, relevance, consistency, and reliability.

Testing, Performance, and Responsible AI

• Develop and execute tests for AI application functionality, integrations, and data-processing pipelines.
• Help investigate issues related to model outputs, application performance, and system integrations.
• Support the monitoring and improvement of AI solution quality, latency, and reliability.
• Follow practices for secure data handling, privacy, responsible AI usage, and protection against inappropriate or unreliable model outputs.
• Document experiments, implementation decisions, test results, and technical findings.

Collaboration and Continuous Learning

• Collaborate with software engineers, AI/ML specialists, QA engineers, and product managers.
• Participate in code reviews, sprint planning, technical discussions, and knowledge-sharing sessions.
• Keep up with developments in AI, machine learning, LLMs, and AI application frameworks.
• Learn and apply new tools, technologies, and approaches based on evolving product and business requirements.

Requirements & Tech Stack

Technical Skills

• Working knowledge of Python and its use in application development or data processing.
• Understanding of programming fundamentals, data structures, algorithms, and object-oriented programming.
• Basic knowledge of machine learning concepts, model training or inference, and AI application use cases.
• Familiarity with APIs, JSON, and integrating external services into applications.
• Basic understanding of data manipulation, preprocessing, and validation.
• Familiarity with Git and standard software development practices.

Preferred (Not Required)

• Exposure to machine learning libraries such as scikit-learn, PyTorch, or TensorFlow.
• Familiarity with LLMs, prompt engineering, AI APIs, or frameworks such as LangChain or LlamaIndex.
• Basic exposure to embeddings, vector databases, semantic search, or RAG architectures.
• Familiarity with SQL, cloud platforms, notebooks, or data-processing pipelines.
• Academic projects, internships, hackathons, or personal projects involving AI, machine learning, NLP, or generative AI.

Qualifications

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related discipline.
• Fresh graduates and candidates with up to 2 years of relevant experience in software development, AI/ML, or data-oriented roles are encouraged to apply.
• Equivalent practical experience demonstrated through relevant projects may also be considered.

What We're Looking For

• Analytical Thinking and Technical Curiosity: Demonstrates interest in AI/ML systems, can break down technical problems, and evaluate solutions.
• Learning Agility: Can quickly understand and apply new technical concepts, is comfortable with evolving technologies, and demonstrates initiative.
• Communication and Collaboration: Clearly communicates technical findings, works effectively with diverse stakeholders, and documents decisions.
• Quality, Ownership, and Responsible AI: Demonstrates attention to detail, commitment to reliable solutions, responsible data handling, and ownership of tasks.