Job Summary
As a Junior Machine Learning Application Engineer, you will support the development and integration of AI-powered systems using Large Language Models (LLMs), Natural Language Processing (NLP), and other machine learning techniques. You’ll work closely with senior engineers to contribute to projects that involve conversational AI, data pipelines, and system performance optimization. This role is ideal for someone early in their career who is eager to learn, contribute, and grow in a fast-paced AI environment.
Key Responsibilities
- Assist in System Architecture Implementation: Support the development and integration of ML components into application architectures under guidance from senior engineers.
- ML Application Support: Help implement and test machine learning applications using tools like Langchain, LlamaIndex, or other LLM frameworks.
- Conversational System Support: Contribute to basic dialogue flows and conversational system design, assisting in flow control and rule-based logic development.
- Data Handling and Preprocessing: Support data preparation for LLM pipelines, including cleaning, formatting, and structuring textual data for training or inference.
- Voice Processing Assistance: Help maintain or test voice components such as speech-to-text (ASR) or text-to-speech (TTS) modules.
- Experimentation and Research: Assist in evaluating AI tools, frameworks, and approaches for performance and feasibility under supervision.
- Testing and Quality Assurance: Write and maintain unit tests and validation scripts to ensure system reliability and functionality.
- Documentation: Maintain project and technical documentation to support reproducibility and team knowledge-sharing.
Qualifications
- Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or related field.
- Familiarity with Python and basic ML/NLP concepts.
- Some exposure to backend development or APIs (e.g., Flask, FastAPI).
- Interest in LLM frameworks like Langchain or LlamaIndex (prior experience is a plus).
- Willingness to learn voice systems, agentic workflows, and conversational AI.
Soft Skills
- Eagerness to learn and improve through feedback.
- Attention to detail and strong documentation habits.
- Team-oriented, proactive communicator.
- Able to manage time and deliver on deadlines with support.
Preferred Experience
- Internship or project experience in ML/NLP or LLM-related work.
- Familiarity with version control (Git), Jupyter, and basic software engineering principles.
Work Environment
Office-based environment with potential for hybrid or remote work depending on company policy.
Reporting
This position will report to the ML Lead.
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