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Mid LevelFull-timePythonAWSREST APIMicroservicesMachine LearningPyTorchGitAIMLNLP
Salary Range
$125,000 - $160,000 /year
About the Role
We are seeking a skilled and motivated Software Engineer focused on Large Language Model (LLM) applications to join our Machine Learning team at Lendbuzz. In this role, you will help design, build, and optimize next-generation conversational agent technologies.
Key Responsibilities
- →Software engineering for LLM-powered conversational agents, with an emphasis on practical implementation, reliability, and user experience
- →Evaluate, fine-tune, and deploy LLM-based models and pipelines using REST APIs and internal microservices
- →Implement prompt engineering, retrieval-augmented generation (RAG), tool-use pipelines, and conversation orchestration logic
- →Investigate and integrate emerging technologies, particularly in real-time voice, streaming, and multi-modal interaction
- →Analyze model outputs, user interactions, and system performance to drive iterative improvements
- →Build and maintain high-quality datasets, including data cleaning, preprocessing, labeling workflows, and benchmarking for NLP tasks
- →Own data quality, ensuring accuracy, reproducibility, and reliability across the data lifecycle
- →Collaborate with ML, backend, and product teams on deployment best practices, monitoring, and scalability of LLM-based services
- →Contribute to internal documentation, experimentation processes, and model evaluation frameworks
Requirements
- ✓Master’s degree in Artificial Intelligence, Computer Science, or a related technical field
- ✓Strong programming skills in Python, with experience in ML and data tooling (e.g., PyTorch, Pandas, NumPy, Scikit-learn)
- ✓Preferred: 2+ years of professional software engineering experience, including scripting, data processing, or backend/ML pipelines
- ✓Experience with NLP techniques, LLMs, or machine learning fundamentals
- ✓Strong problem-solving ability and comfort working independently in a fast-moving environment
- ✓Preferred: Experience deploying applications or models on cloud platforms, preferably AWS
- ✓Bonus (not required): experience with real-time systems, WebSockets/streaming, RAG pipelines, vector databases, or ML evaluation frameworks, Genesys/Twilio
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