Senior AI / Machine Learning Engineer (LLMs & Python Backend)

About the role

We are looking for a Senior AI / Machine Learning Engineer to design, build, and operate intelligent systems that combine large language models (LLMs) with traditional machine‑learning techniques. The role involves full‑stack AI development, from data preparation and model training to deployment, monitoring, and continuous improvement, tightly integrated with Python‑based backend services.

Key responsibilities

• Design, train, evaluate, and deploy ML models for real‑world use cases.
• Fine‑tune and integrate LLMs for conversational AI, extraction, classification, and decision support.
• Choose appropriate approaches (LLMs, traditional ML, rule‑based or hybrid) for each problem.
• Build end‑to‑end ML pipelines: data ingestion, feature engineering, training, validation, and inference.
• Monitor model performance, drift, and accuracy over time.
• Integrate LLMs into applications such as chatbots, NLU modules, structured data extraction, and content generation.
• Implement prompt engineering, output validation, and guardrails to reduce hallucinations.
• Develop and maintain scalable Python backend services (e.g., FastAPI), ensuring robust error handling, logging, and observability.
• Optimize latency, cost, and reliability of AI‑powered services.

Required profile

• 4+ years of professional experience in software engineering and/or machine learning.
• Strong proficiency in Python and hands‑on experience with model development.
• Experience integrating LLMs or NLP systems into production applications.
• Solid understanding of data structures, algorithms, and system design.
• Proven ability to deploy models and services in production environments.

Required skills

• Python
• FastAPI (or similar framework)
• REST API development
• Async and concurrent programming
• SQL databases
• Redis or similar caching systems
• Cloud or container‑based deployments
• Supervised and unsupervised learning
• Natural Language Processing (NLP) and conversational AI
• Model evaluation and monitoring
• Feature engineering and data preprocessing
• Model fine‑tuning and experimentation
• Speech‑to‑text (nice‑to‑have)
• Vector databases or semantic search (nice‑to‑have)