Senior Python Dev. (AI Engineer)
RemoteArgentina
Job Summary
Architect and develop scalable, production-grade Python applications, including LLM-powered systems, RESTful APIs, and microservices using FastAPI, Flask, or Django. Design and implement end-to-end Retrieval-Augmented Generation (RAG) pipelines, embedding workflows, and semantic search solutions. Build, optimize, and maintain data ingestion pipelines and background task processing using Celery, RabbitMQ, or Kafka. Integrate and manage LLM APIs and AI platforms within robust Python service architectures. Design and optimize database schemas across relational and NoSQL databases, and containerize services using Docker and Kubernetes for cloud deployments. Identify and resolve performance bottlenecks through profiling and code optimization. Work on long-term projects with one project at a time, utilizing flexible working hours and remote options.
Required Qualifications
- 7+ years of software development experience, with the latest 1–2 years focused on AI/LLM-powered solutions
- Strong, expert-level Python skills — Python is the primary language for this role
- Proven hands-on experience building and deploying LLM applications, especially RAG-based systems
- Good knowledge of Python web frameworks and libraries: Django, Flask, FastAPI
- Experience with data-processing libraries: Pandas, NumPy, Scikit-learn
- Solid understanding of vector databases (e.g., Pinecone, Weaviate) and semantic search architectures
- Experience integrating LLM APIs (OpenAI, Anthropic, Azure OpenAI)
- Strong understanding of RESTful APIs, microservices, and scalable backend architecture
- Good working knowledge of cloud platforms: AWS, Azure, or GCP
- Solid experience with SQL and relational databases (PostgreSQL, MySQL)
- Experience with messaging queues (RabbitMQ, Kafka) and Docker
- Strong testing, debugging, and problem-solving skills
- Strong communication skills; Intermediate English or higher
- BSc/MSc in Computer Science, Engineering, AI, or a related field
Desired Qualifications
- Experience with multi-agent AI system design
- Familiarity with alternative vector databases (FAISS, Milvus)
- Experience with the Hugging Face ecosystem or fine-tuning open-source models
- Hands-on experience with advanced MLOps frameworks and model governance
- Experience with system architecture or leading a software team
- PhD (completed or in progress) in a relevant field
- Experience with NoSQL databases (MongoDB, DynamoDB) — a plus
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