Senior AI/ML Engineer

Svitla Systems Inc. is looking for a Senior AI/ML Engineer for a full-time position (40 hours per week) in Argentina. Our client is a composite AI platform that analyzes telemetry, identifies patterns and common causes, and prioritizes issues based on user behavior. It uses machine learning and Generative AI to provide contextual insights and recommendations, even when model confidence is low.

REQUIREMENTS

• •7+ years of professional experience in Machine Learning, Data Science, or AI Engineering roles, with a track record of shipping systems to production.
• •Strong proficiency in Python and hands-on experience with ML/DL frameworks such as PyTorch, TensorFlow, and Scikit-learn.
• •Proven experience building Generative AI / LLM-based solutions: RAG architectures, prompt engineering, embeddings, and LLM orchestration frameworks (e.g., LangChain).
• •Hands-on experience with vector databases and semantic/hybrid search (OpenSearch, pgVector, Elasticsearch, or similar).
• •Experience designing and deploying end-to-end ML pipelines, including experiment tracking and model registry tools (e.g., MLFlow).
• •Practical experience with multi-agent AI architectures, structured output validation, and/or LLM evaluation frameworks.
• •Experience with at least one major cloud platform (AWS or Azure) and its AI/ML services (e.g., AWS Lambda, SageMaker, OpenSearch, or Azure OpenAI, Azure ML).
• •Experience with containerization (Docker); familiarity with orchestration (Kubernetes, ECS, EKS, or Fargate) is a plus.
• •Solid SQL skills and experience with relational or data-warehouse platforms (PostgreSQL, Snowflake, BigQuery, Redshift, or similar).
• •Experience building and exposing APIs for model serving (FastAPI or similar).
• •Proficiency with Git and collaborative development workflows.
• •Strong problem-solving, communication, and English-language skills, with the ability to work effectively with US-based, cross-time-zone teams.
• •Advanced degree (MSc/PhD) in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
• •Experience with Hugging Face Transformers, sentence-transformers, or spaCy.
• •Experience fine-tuning LLMs for specialized tasks (translation, classification, domain adaptation).
• •Experience with Computer Vision or audio/speech processing.
• •Experience with Databricks and the medallion (bronze/silver/gold) architecture.
• •Experience implementing observability/monitoring and automated model-retraining pipelines.
• •Background delivering AI solutions in an enterprise or regulated environment (martech, fintech, healthcare, etc.).
• •Experience working in a remote environment with team members across different time zones.

RESPONSIBILITIES

• Design, develop, and deploy end-to-end Machine Learning pipelines (training → registry → serving → monitoring) for production use cases.
• Build and maintain Generative AI and LLM-based solutions, including RAG (Retrieval-Augmented Generation) architectures, prompt engineering, and embeddings pipelines.
• Design and implement multi-agent AI systems for document processing, structured data extraction, entity resolution, and complex reasoning workflows.
• Architect and maintain semantic search and retrieval systems using vector databases such as OpenSearch, pgVector, or similar.
• Develop predictive ML models (classification, regression, customer value/lifetime prediction, bidding and optimization systems, etc.) grounded in solid feature engineering practices.
• Fine-tune and evaluate LLMs, including dataset preparation, quality-metric definition, few-shot learning, and transfer-learning approaches.
• Implement MLOps best practices: experiment tracking, model registry, CI/CD for ML, observability, monitoring, and drift detection.
• Deploy and operate ML/AI workloads on cloud-native infrastructure (AWS and/or Azure), leveraging containerization and scalable serving patterns.
• Collaborate with cross-functional teams - product, data, and engineering - to translate business problems into scalable, well-architected AI solutions.
• Document architecture decisions, experiments, and results clearly for both technical and non-technical stakeholders.
• Participate in code reviews, architecture discussions, and knowledge-sharing within the engineering team.