Top Machine Learning Jobs in Romania

Lead Data, Analytics, AI, and GenAI presales activities for strategic enterprise opportunities. Facilitate discovery workshops, translate business needs into scalable architectures, lead RFP and RFI responses, and present solutions to executives. Design end-to-end AI and cloud architectures across Azure, GCP, Databricks, and open-source ecosystems. Advise on AI transformation use cases, roadmaps, governance, and implementation, while mentoring delivery teams and developing reusable frameworks, offerings, thought leadership, and customer success materials.

Build Solidgate’s new ML/AI function, developing production machine learning models, recommendation systems, and real-time transaction-routing decision systems. Own experimentation from ideation through A/B testing and deployment, engineer features from high-cardinality transactional data, integrate emerging AI technologies, and collaborate with Product, Engineering, and Data Platform teams on training and analytics pipelines.

Develop and deploy machine learning and statistical models that solve business problems and create measurable impact. Responsibilities include exploratory data analysis, feature engineering, experimentation, model optimization, explainability, MLOps, prompt engineering, and cloud-based data collaboration. The role involves working with clients and stakeholders, communicating insights through visualization and storytelling, and contributing to scalable analytics, Generative AI, and data science solutions.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Research and investigate global fraud trends using BioCatch data and external sources. Produce data-driven insights, visualizations, written thought leadership, dashboards, and presentations. Build repositories and automate analysis workflows while applying fraud expertise and AI tools to validate findings and support sales and marketing.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Build and scale MLOps infrastructure that moves machine learning models from development to production. Responsibilities include model serving, automated training and deployment, CI/CD, monitoring, drift detection, rollback mechanisms, streaming feature pipelines, and agentic workflow integration. The role requires collaboration with data science, platform, and product teams to deliver reliable, observable, low-latency ML systems.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Design, back-test and implement pricing, risk management, and agency trading strategies; collaborate with developers to integrate models into the trading platform; participate in product ideation, modeling, parameter tuning, and risk evaluation.

Lead technical strategy and architecture for large-scale production systems. Design and deliver scalable personalization, search, recommendation, and ML-powered features. Partner with Product, Data Science, and Design. Influence cross-team technical decisions, mentor senior engineers, and improve engineering quality, reliability, and productivity through tooling and best practices.

Develop and operate production NLP and LLM-powered microservices for biomedical data analysis. Integrate OpenAI, Claude, and Hugging Face APIs using prompt engineering and RAG; build ETL pipelines, improve entity and relationship extraction, develop literature-processing models, and ensure system testing, reliability, observability, scalability, and cost efficiency.

Architect and build core Decision Intelligence platform capabilities: heterogeneous data integration, semantic modeling, simulation and optimization services, scalable distributed backend systems, AI/ML and LLM-powered production deployments, data pipelines, and operational tooling. Provide technical leadership, own full lifecycle from design to production, improve reliability, observability, and drive engineering excellence.

Own Go-To-Market for performance media buyers: build relationships, close and onboard clients, represent Kayzen at industry events, advise prospects on product strategy, and funnel market feedback across teams.

The Head of Education & Community at Siena is responsible for developing product education, onboarding programs, community events, and market education resources. This role involves creating curricula, conducting workshops, and supporting sales with training and demos while leveraging AI for teaching efficiency.

Lead cloud application architecture and migration strategy for large-scale Azure programs. Define architecture principles, govern design, drive cloud migration roadmaps, mentor teams, evaluate technologies and vendors, and ensure agile, secure, scalable microservices and data solutions with minimal business disruption.

Drive product adoption and customer outcomes via 1:1 and 1:many channels. Onboard and support scale customers, manage escalations, monitor health indicators, collaborate cross-functionally, run webinars and calls, prioritize high-volume tasks, and help mitigate churn while identifying expansion opportunities.

Design, build, deploy, and maintain production-grade machine learning systems for a proactive AI platform. Own projects end to end across data curation, model training, evaluation, inference, monitoring, and optimization. Translate research into reliable systems, troubleshoot live production issues, improve performance iteratively, and balance latency, cost, safety, and reliability. Collaborate with research, engineering, and product teams while mentoring engineers and conducting technical reviews.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

Develop, deploy, and maintain scalable AI/ML pipelines and production models, including LLMs and generative AI. Optimize model performance, latency, and cost across AWS, GCP, and Azure. Apply MLOps practices such as CI/CD, versioning, monitoring, logging, and maintenance. Collaborate with data scientists, software engineers, solution architects, and cross-functional teams to integrate AI capabilities into products and workflows.

Design and build state-of-the-art web applications using Java/Kotlin and Spring Boot. Follow agile, TDD and clean-code practices, mentor peers, work with clients, and contribute to projects including ML cloud detection, public portals, and consumer apps.

Lead and mentor functional safety engineers to define, analyze, and validate ASIL safety requirements for automotive domain controllers, displays, and connectivity. Perform safety analyses (HARA, FTA, FMEDA, FMEA, STPA), align safety with cybersecurity and ML considerations, coordinate verification/validation activities, and integrate safety concepts with development partners and suppliers.

Design, develop, and maintain data pipelines and large-scale processing systems to ensure data accessibility, reliability, and efficient processing. Build ETL/ELT flows, integrate multiple data sources, support data warehousing and reporting, implement APIs and orchestration, optimize data architectures, and apply algorithmic/statistical methods (including AI) to enable analysis and downstream consumption.

Design and implement AI/ML compiler technologies for embedded ARM and RISC-V hardware using C++ and LLVM. Develop MLIR and graph compiler features, optimize model lowering, fusion, tiling, scheduling, code generation, and inference performance. Collaborate with hardware architects, ML researchers, and global development teams to support automotive system-on-chip platforms. Analyze generated code with simulators and profilers while contributing to open-source compiler technologies.

Develop AI/ML compiler technologies for embedded ARM and RISC-V processors, optimizing models through graph compilation, lowering, fusion, tiling, scheduling, code generation, and performance analysis. The role involves C++ and LLVM/MLIR development, collaboration with hardware architects and ML researchers, compiler support for automotive SoCs, and contribution to open-source projects.