11/11/2026 updated
100 % available
Senior AI/ML Engineer & Data Scientist | Generative AI & LLM Solutions
Tallinn, Estonia
Only remote
Master of Science (M.Sc.) in Computer Science and Artificial IntelligenceAbout me
Senior AI/ML Engineer and Data Scientist with 10+ years of experience building production-ready ML, deep learning, and Generative AI solutions. Specialized in LLMs, agentic systems, RAG architectures, and scalable AI engineering.
Artificial IntelligencePython (Programming Language)Machine LearningAzure Machine LearningSolution ArchitectureLifecycle ManagementFeature EngineeringData ScienceLarge Language ModelsPrompt EngineeringDeep LearningGenerative AIMachine Learning OperationsArtificial Intelligence Markup Language (AIML)
Generative AI & LLM Engineering
Expertise in LLM application architecture, prompt engineering, LangChain, LangGraph, MCP, tool calling, planning, memory-augmented systems, multi-agent orchestration, ReAct, structured outputs, guardrails, and prompt injection defense. Design and deployment of production-grade Generative AI and LLM solutions achieving significant reductions in response time.
Machine Learning & Deep Learning
Proficiency in PyTorch, scikit-learn, NumPy, feature engineering, supervised and unsupervised learning, classification, regression, clustering, ensemble methods, hyperparameter optimization, model evaluation, and explainable AI. Application of Transformers, CNNs, RNNs, LSTMs, attention mechanisms, transfer learning, custom neural network architectures, and diffusion models.
RAG & Information Retrieval
Hands-on experience with agentic RAG, dense and sparse retrieval, hybrid search, document chunking, embeddings, cross-encoders, reranking, late chunking, retrieval evaluation, FAISS, pgvector, and Qdrant. Development of LLM-powered assistants and RAG systems supporting collections of 10,000+ documents.
Programming & Backend Development
Strong command of Python, FastAPI, PostgreSQL, Redis, MongoDB, NATS JetStream, Kafka, REST APIs, Linux, and Git for building scalable backend applications and ML-enabled services.
Fine-Tuning & Optimization
Application of Parameter-Efficient Fine-Tuning (PEFT), LoRA, QLoRA, instruction tuning, Mistral model fine-tuning, CUDA, and multi-GPU model training and serving to optimize model performance.
MLOps & AI Observability
Experience with model testing, deployment, monitoring, lifecycle management, Docker, OpenTelemetry, LangSmith, Langfuse, Promptfoo, and LLM evaluation and A/B testing to ensure reliable AI delivery.
AI Engineering & Delivery
Capabilities in AI solution architecture, full-stack AI development, agentic coding tools, production integration, technical documentation, responsible AI practices, and stakeholder management.
Data Science & Analytics
End-to-end machine learning pipelines covering data preparation, feature engineering, model training, evaluation, and deployment. Conduction of 10+ model and retrieval experiments using quantitative metrics, error analysis, and representative evaluation datasets.
Expertise in LLM application architecture, prompt engineering, LangChain, LangGraph, MCP, tool calling, planning, memory-augmented systems, multi-agent orchestration, ReAct, structured outputs, guardrails, and prompt injection defense. Design and deployment of production-grade Generative AI and LLM solutions achieving significant reductions in response time.
Machine Learning & Deep Learning
Proficiency in PyTorch, scikit-learn, NumPy, feature engineering, supervised and unsupervised learning, classification, regression, clustering, ensemble methods, hyperparameter optimization, model evaluation, and explainable AI. Application of Transformers, CNNs, RNNs, LSTMs, attention mechanisms, transfer learning, custom neural network architectures, and diffusion models.
RAG & Information Retrieval
Hands-on experience with agentic RAG, dense and sparse retrieval, hybrid search, document chunking, embeddings, cross-encoders, reranking, late chunking, retrieval evaluation, FAISS, pgvector, and Qdrant. Development of LLM-powered assistants and RAG systems supporting collections of 10,000+ documents.
Programming & Backend Development
Strong command of Python, FastAPI, PostgreSQL, Redis, MongoDB, NATS JetStream, Kafka, REST APIs, Linux, and Git for building scalable backend applications and ML-enabled services.
Fine-Tuning & Optimization
Application of Parameter-Efficient Fine-Tuning (PEFT), LoRA, QLoRA, instruction tuning, Mistral model fine-tuning, CUDA, and multi-GPU model training and serving to optimize model performance.
MLOps & AI Observability
Experience with model testing, deployment, monitoring, lifecycle management, Docker, OpenTelemetry, LangSmith, Langfuse, Promptfoo, and LLM evaluation and A/B testing to ensure reliable AI delivery.
AI Engineering & Delivery
Capabilities in AI solution architecture, full-stack AI development, agentic coding tools, production integration, technical documentation, responsible AI practices, and stakeholder management.
Data Science & Analytics
End-to-end machine learning pipelines covering data preparation, feature engineering, model training, evaluation, and deployment. Conduction of 10+ model and retrieval experiments using quantitative metrics, error analysis, and representative evaluation datasets.
Experience
Professional experience10+ years
Freelancer since2015
Languages
EnglishFluent
Project history
Led the design and deployment of production-grade Generative AI and LLM solutions, targeting an 89% reduction in response time. Designed multi-agent AI systems using Python, LangGraph, FastAPI, PostgreSQL, Qdrant, and NATS JetStream, integrating 5+ tools and data sources. Improved LLM output quality by 56% through prompt engineering, structured evaluations, and iterative testing. Automated 10+ business workflows using LLMs, agent orchestration, and API integrations. Built automated evaluation, regression-testing, and observability workflows using LangSmith, Langfuse, Promptfoo, and OpenTelemetry. Evaluated LLM providers and model configurations to balance accuracy, latency, and inference cost. Established AI engineering standards and responsible AI practices with cross-functional teams.
Designed and delivered 3-5 machine learning and Generative AI solutions for enterprise client projects, translating business requirements into deployable AI applications. Developed LLM-powered assistants and RAG systems for document understanding, knowledge discovery, and domain-specific question answering, supporting collections containing 10,000+ documents. Improved retrieval relevance by 20% through hybrid search, embedding optimization, metadata filtering, and cross-encoder reranking. Built end-to-end machine learning pipelines covering data preparation, feature engineering, model training, evaluation, and deployment. Applied PyTorch and Transformer architectures to develop, fine-tune, and evaluate deep learning models for specialized business use cases. Conducted 10+ model and retrieval experiments using quantitative metrics, error analysis, and representative evaluation datasets. Integrated AI components with existing backend services, APIs, and enterprise data systems. Introduced standardized model evaluation and technical documentation practices.
Developed Python-based backend applications and ML-enabled services, integrating predictive capabilities into business software and data-driven workflows. Integrated 3+ machine learning models into application backends through REST APIs. Optimized data preprocessing, SQL queries, and inference workflows, reducing processing time by 30%. Implemented feature engineering, model training, and evaluation pipelines for classification, regression, and predictive analytics use cases. Applied PyTorch to experiment with neural network architectures, Transformer-based models, custom loss functions, and hyperparameter optimization. Containerized applications using Docker, reducing manual deployment steps by 50%. Integrated PostgreSQL, Redis, and MongoDB into backend services.