AI Engineer at Accesa

An experienced AI Engineer is needed to build and deploy AI-driven solutions across Machine Learning and Generative AI projects. The role involves developing production-ready applications, implementing LLM-powered systems, improving AI performance, and integrating intelligent solutions into enterprise environments.

Location: Remote, Romania
Job Type: Full-Time
Department: Core Technologies | Data & AI

Job Responsibilities

• Develop and maintain AI solutions using Machine Learning and Generative AI technologies
• Build and improve LLM-powered applications, including prompt engineering and orchestration workflows
• Design and optimize RAG pipelines, including embeddings, retrieval strategies, and semantic search capabilities
• Work with vector databases such as pgvector, Pinecone, FAISS, or Weaviate
• Train, evaluate, and deploy Machine Learning models for business use cases
• Develop APIs and integrate AI capabilities into enterprise applications
• Deploy and manage AI workloads on Azure, AWS, or GCP environments
• Implement monitoring, validation, and reliability measures for AI systems
• Collaborate with software engineers, data scientists, and stakeholders to deliver scalable AI solutions
• Stay updated with emerging AI, ML, and Generative AI technologies

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Job Requirements

• 2–4 years of experience in Python development using libraries such as Pandas, NumPy, and Scikit-learn
• Hands-on experience building and deploying Machine Learning models
• Practical experience working with LLM applications and prompt engineering
• Experience with LangChain or similar orchestration frameworks
• Knowledge of RAG pipeline implementation, including chunking, embeddings, and retrieval
• Experience using vector databases such as pgvector, Pinecone, FAISS, or Weaviate
• Understanding of embeddings, semantic search, and information retrieval concepts
• Experience building and consuming APIs for integrations
• Hands-on experience with cloud platforms such as Azure, AWS, or GCP
• Understanding of logging, monitoring, and observability practices
• Strong communication and teamwork skills

Nice to Have

• Experience with PyTorch or TensorFlow
• Familiarity with MLOps tools such as MLflow, Airflow, or Kubeflow
• Knowledge of AI safety and LLM evaluation frameworks
• Experience with Java, Spring, or backend technologies
• Familiarity with frontend technologies for AI applications
• Experience with real-time AI/ML systems or enterprise data platforms