Accesa
Information Technology And Services
AIEngineer
Neural analysis suggests this role is
optimal for mid candidates.
“AI Engineer at Accesa. Skills: Machine Learning, Generative AI, LLM, RAG. Develop AI solutions. Maintain AI solutions”
Industry & Context.
Problem-solving
What They're Looking For.
Must Have
2 - 4 years Python development, Pandas, NumPy, Scikit-learn experience, Build and deploy ML models, Practical LLM application experience, Prompt engineering experience, LLM integration experience, LangChain or similar experience, Design or implement RAG pipelines, Document chunking experience, Embeddings experience, Retrieval strategies experience, pgvector, FAISS, Pinecone, or Weaviate experience, Understand embeddings, Understand semantic search, Understand information retrieval, MCP or similar tool-integration experience, Build and consume APIs, Deploy and operate AI/ML services, Azure, AWS, or GCP experience, Cloud platforms working knowledge, On-premise deployment models knowledge, Logging fundamentals understanding, Monitoring fundamentals understanding, Observability fundamentals understanding, Good communication skills, Work effectively in cross-functional teams
Nice to Have
PyTorch or TensorFlow experience, MLOps tools and practices familiarity, AI safety, guardrails, or evaluation frameworks experience, Backend technologies experience, Java or Spring experience, Frontend technologies experience, Real-time or streaming AI/ML systems familiarity, Enterprise data platforms experience, Data engineering pipelines experience
What You'll Do.
Maintain AI solutions
Build LLM-based applications
Improve LLM-based applications
Implement RAG pipelines
Optimize RAG pipelines
Work with vector databases
Contribute to API development
Integrate AI capabilities
Implement basic guardrails
Implement validation mechanisms
Implement monitoring mechanisms
Configure AI workloads
Collaborate with teams
Contribute to knowledge sharing
How You'll Work.
Team & Collaboration
Cross-functional teams; Software engineers; Data scientists; Business stakeholders
Communication Scope
Good communication skills
Full Job Description
Accesa is a leading technology company headquartered in Cluj-Napoca, with offices in Oradea and 20 years of experience in turning business challenges into opportunities and growth. A value-driven organisation, it has established itself as a partner of choice for major brands in Retail, Manufacturing, Finance, and Banking. It covers the complete digital evolution journey of its customers, from ideation and requirements setup to software development and managed services solutions. With more than 1,200 IT professionals, Accesa also has a fast-growing footprint, establishing itself as an employer of choice for IT professionals who are passionate about problem-solving through technology. Coming together in strong tech teams with a customer-centric approach, they enable businesses to grow, delivering value for our clients, partners, industry, and community. We are looking for a hands-on AI Engineer to build production-ready AI solutions across Machine Learning and Generative AI use cases. The role focuses on implementation and technical delivery: developing LLM-powered applications, building ML models, integrating AI capabilities into enterprise systems, and improving the reliability, performance, and maintainability of deployed solutions. Responsibilities Develop and maintain AI solutions using both Machine Learning and Generative AI approaches, aligned with business and client needs. Build and improve LLM-based applications, including prompt engineering, orchestration flows, and tool usage. Implement and optimizeRAG pipelines, including chunking, embeddings, ranking, and retrieval strategies. Work with vector databases such as pgvector, FAISS, Pinecone, or Weaviate to support semantic search and context-aware responses. Develop integrations using MCP or similar approaches to connect AI systems with external tools, APIs, and enterprise platforms. Train, evaluate, and deploy ML models for prediction, classification, clustering, or other data-driven use cases. Contribute t
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