AI Engineer
Develop and validate statistical programming code to support clinical trial data analysis and reporting. Collaborate with data scientists and statisticians to ensure data requirements and regulatory standards are met.
Remote AI Engineer Jobs in Bulgaria
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Develop and validate statistical programming code to support clinical trial data analysis and reporting. Collaborate with data scientists and statisticians to ensure data requirements and regulatory standards are met.
The AI Engineer will design, build, and optimize machine learning models and AI solutions to support enterprise automation and intelligent decision-making. They will also collaborate with cloud and engineering teams to deploy models and manage data pipelines.
You will build tools to evaluate agent behavior, analyze traces, and implement features for automatic self-improvement. Additionally, you will run experiments on Mastra primitives to optimize configuration and performance.
Design, develop, and deploy multi-agent AI systems and RAG pipelines for reasoning and task execution. Collaborate with engineering teams to integrate data sources and implement observability frameworks for model performance monitoring.
The AI Engineer will design, develop, and maintain production-grade AI services and APIs using Python and modern orchestration frameworks. They will also build and optimize end-to-end RAG solutions while ensuring system quality through observability, evaluation, and performance monitoring.
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The AI Engineer will lead the development and deployment of internal AI agents, intelligent search tools, and automated business workflows. They are responsible for integrating AI models with existing SaaS platforms and establishing robust infrastructure, security, and governance for AI initiatives.
The AI Engineer will design, build, and deploy production-ready AI features including LLM-backed workflows and retrieval systems. They will also own the data path, define evaluation metrics, and monitor deployed systems for quality and performance.
Design, build, and deploy production-ready AI applications including RAG-powered solutions and conversational agents on major cloud platforms. Collaborate with cross-functional teams to automate insurance workflows such as claims and underwriting.
The candidate will serve as an AI Engineer. Specific duties are not detailed in the posting.
Design, develop, and deploy autonomous AI agents and multi-agent systems to drive intelligent decision-making and workflow automation. Collaborate with cross-functional teams to architect scalable agentic platforms and implement robust monitoring and safety guardrails.
Develop AI agents and automation workflows using LLMs, LangChain, and LangGraph. Implement RAG pipelines and integrate AI solutions with internal systems, APIs, and databases.
Evaluate AI-generated coding interactions to assess the quality, reasoning, and engineering judgment of modern coding agents. Provide clear, opinionated written feedback and video explanations to help define high-quality engineering standards.
You will build and optimize large-scale data pipelines to support AI model training and next-generation product development. You will collaborate with AI scientists to discover new data sources and ensure the scalability and quality of training datasets.
The Applied AI Engineer will design, build, and deploy LLM-powered features and agents from prototype to production. They will also be responsible for implementing RAG, monitoring system performance, and collaborating with product teams to define technical criteria.
You will build and optimize large-scale data pipelines to support AI model training and next-generation product development. This involves discovering new audio data sources and collaborating with AI scientists to improve data quality and system scalability.
You will build and optimize large-scale data pipelines to support model training and next-generation AI products. You will also collaborate with AI scientists to improve dataset quality and ensure efficient, scalable data processing systems.
You will build and optimize large-scale data pipelines to support AI model training and next-generation product development. You will also collaborate with AI scientists to discover new data sources and ensure high-quality, scalable data processing.
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Evaluate AI-generated coding interactions to assess the quality, reasoning, and engineering judgment of modern coding agents. Provide clear, opinionated written feedback and video walkthroughs to help define high-quality AI-assisted development standards.
Evaluate the quality of AI-generated coding interactions to ensure they reflect strong engineering judgment and reasoning. Provide clear, opinionated written and video feedback to help define high standards for AI-assisted development.
Evaluate AI-generated coding interactions to assess the quality, reasoning, and engineering judgment of modern coding agents. Provide clear, opinionated written and video feedback to help define high-quality standards for AI-assisted development.
Evaluate AI-generated coding interactions to assess the quality of reasoning, engineering judgment, and overall helpfulness. Provide clear, opinionated written and video feedback to help define high-quality standards for AI coding agents.
Evaluate the quality, reasoning, and engineering judgment of AI-generated coding interactions. Provide clear, opinionated written and video feedback to help define high-quality standards for AI coding agents.
You will lead a squad of engineers to build cloud-native, AI-powered digital products while remaining hands-on with frontend, backend, and AI integration tasks. The role involves driving technical decisions, setting engineering standards, and collaborating with US-based clients across time zones.
Design and build agentic AI platform capabilities from the ground up while developing cloud-native enterprise applications. Collaborate with client architects and engineering leaders to deploy and operate solutions within regulated environments.
You will bridge the gap between functional design and technical implementation by creating low-level designs and prototype code for agentic AI systems. This involves running independent proofs of concept, proving integration patterns, and documenting solutions for a distributed development team.
Design, develop, and deploy AI-driven applications and workflows on the ServiceNow platform. Interface with stakeholders to gather requirements and manage the full application development lifecycle including coding, testing, and troubleshooting.
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The Forward Deployed Engineer will lead end-to-end implementations of the SOC for AI platform in enterprise environments, managing project timelines and technical deliverables. They will act as the primary liaison between client stakeholders and internal teams while configuring security policies and custom Python workflows for AI agents.
The role involves leading the development of enterprise Model Context Protocol (MCP) integrations and establishing governed multi-model access. You will be responsible for building reference servers, connecting agentic workflows, and implementing model evaluation and observability frameworks.
Design and maintain production-grade agentic systems, including multi-agent orchestration and RAG pipelines grounded in company data. Implement AI infrastructure on AWS while ensuring compliance, safety, and high-quality output through rigorous evaluation frameworks.
The engineer will lead the transition of an existing customer solution from traditional low-code flows to a fully agentic architecture. They will be responsible for designing, implementing, and monitoring autonomous task orchestration and multi-agent patterns within the Cognigy platform.
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