This article outlines the role and technical scope for developing production-grade LLM chatbots for customer support. It details core responsibilities—training and fine-tuning in-house LLMs, building prompt engineering and RAG pipelines, preprocessing conversation data, integrating with backend APIs—and the required skills, desirable experience, and the tangible gains and candidate profile for this AI-focused opportunity.
Core Responsibilities and Technical Workstream
The primary focus is on creating reliable, accurate LLM-based customer support systems. Responsibilities include:
- Train, fine-tune, and improve in-house LLMs: Iteratively train and fine-tune models to handle customer support use cases, improving accuracy, consistency, and tone over time.
- Develop and test prompt engineering and RAG pipelines: Design prompts, retrieval-augmented generation flows, and pipelines that combine LLM outputs with retrieved knowledge to produce relevant responses.
- Build response evaluation frameworks: Create automated and human-in-the-loop evaluation schemes to measure response quality, reduce hallucinations, and guide model improvements.
- Preprocess and structure real customer conversation data: Prepare and format conversational datasets for fine-tuning, embeddings, and retrieval, ensuring data supports accurate and consistent model behavior.
- Reduce hallucinations and improve response properties: Focus on strategies that increase factual accuracy, stable tone, and consistent behavior in customer-facing replies.
- Integrate LLMs with backend services using APIs: Connect models to product and service backends so responses can access or trigger backend functionality via APIs.
- Analyze model failures and implement data-driven fixes: Investigate failure cases, derive actionable insights, and apply data-centric improvements to models and pipelines.
- Document experiments and system designs: Maintain clear records of experiments, findings, and architectural decisions to enable reproducibility and team knowledge transfer.
Skills, Experience, and What You’ll Gain
To execute the above workstream effectively, candidates should bring the following capabilities and will benefit from hands-on exposure:
- Required technical skills: Strong proficiency in Python and a solid understanding of machine learning and NLP fundamentals; hands-on experience with LLM workflows—fine-tuning, embeddings, prompting, and RAG; experience with PyTorch or TensorFlow; familiarity with frameworks such as Hugging Face, LangChain, or LlamaIndex; and an understanding of transformer-based architectures.
- Ability to implement research ideas: Convert research concepts into working implementations that can be tested, evaluated, and iterated in a production context.
- Good-to-have experience: Building chatbots or conversational AI, working with vector databases (FAISS, Pinecone, Weaviate), LLM evaluation with automated and human-in-the-loop metrics, exposure to cloud platforms (AWS/GCP/Azure), and prior experience with SaaS or production ML systems.
- What you’ll gain: End-to-end exposure to production-grade LLM chatbot development, mentorship from engineers working on real-world AI systems, practical experience with deployment, monitoring, and iteration, strong potential for full-time conversion based on performance, and a meaningful AI project to showcase.
- Who should apply: Students or recent graduates in CS, AI, Data Science, or related fields; candidates with demonstrable LLM or NLP projects; and individuals who enjoy debugging models and improving real system behavior.
In summary, this opportunity centers on building and refining in-house LLMs for customer support through fine-tuning, prompt engineering, RAG, evaluation, and API integration. Candidates with strong Python, ML/NLP fundamentals, hands-on LLM experience, and a drive to convert research into production will gain practical, production-grade experience, mentorship, and potential for longer-term roles.
