Software Engineer delivering MLOps solutions for Generative AI at DataGalaxy. Focusing on reliability and collaboration with product engineering teams in a hybrid environment.
About the role
- Software Engineer focused on developing LLM applications for conversational agents in a hybrid setting. Collaborating with ML researchers and product teams in a dynamic environment.
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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