AI Engineer (LLM & Applied ML)

We are looking for an AI Engineer with deep expertise in Large Language Models (LLMs) and NLP to develop LLM-powered applications. You will take ownership of designing, building, evaluating, and deploying production-grade AI systems.

Beyond the initial LLM/NLP focus, this role requires strong general machine learning foundations and the ability to solve a variety of applied problems (e.g., regression/classification on structured/tabular data), as future business needs evolve.

Responsibilities:

• Work with stakeholders to understand business goals, requirements, constraints, and available data.
• Design and build LLM-based AI systems, including retrieval-augmented generation (RAG), fine-tuning pipelines, and agent/tool-using workflows.
• Develop robust evaluation methods for LLM applications (e.g., relevance, groundedness, safety, latency, cost) and drive iterative improvement.
• Build scalable data pipelines for text and structured datasets, including preprocessing, labeling strategies, and quality checks.
• Develop and deploy machine learning models for non-NLP use cases when required (e.g., prediction on tabular data).
• Own the full model lifecycle: experimentation, training, validation, deployment, monitoring, and continuous optimization.
• Implement production-grade ML engineering practices: versioning, testing, observability, and performance monitoring.
• Collaborate with engineering and product teams to integrate AI capabilities into core systems and services.
• Research and prototype new techniques in LLMs, NLP, and applied ML, and translate useful advances into production.

Requirements:

• Experience in AI/ML engineering, NLP engineering, or data science with production delivery.
• Strong background in LLMs and NLP, including hands-on experience with several of:
  • RAG architectures and retrieval optimization
  • Embeddings, semantic search, and reranking
  • Prompt engineering and structured prompting
  • Fine-tuning/adaptation
  • Agentic AI or tool/function-calling systems
• Solid understanding of machine learning fundamentals, statistics, and model evaluation.
• Strong Python skills and experience with ML libraries
• Experience deploying ML/LLM systems in production.
• Ability to drive projects end-to-end, from problem framing to production impact.
• Strong communication skills and comfort working with both technical and non-technical stakeholders.