Accesa
Tech / AI / Software
AIEngineer
Neural analysis suggests this role is
optimal for mid candidates.
“AI Engineer at Accesa. Skills: Machine Learning, Generative AI, LLM-based applications, RAG pipelines, Vector databases, Cloud platforms (Azure, AWS, GCP). Develop and maintain AI solutions using both Machine Learning and Generative AI approaches. Build and improve LLM-based applications”
What You'll Achieve.
Deliver production-ready AI solutions; Improve reliability, performance, and maintainability of deployed solutions; Enable businesses to grow; Deliver value for clients, partners, industry, and community
Industry & Context.
Problem-solving through technology
What They're Looking For.
Must Have
2 - 4 years of experience in Python development, Hands-on experience building and deploying Machine Learning models in real-world use cases, Practical experience with LLM-based applications, including prompt engineering and LLM integration, Experience with frameworks such as LangChain or similar orchestration tools, Experience designing or implementing RAG pipelines, including document chunking, embeddings, and retrieval strategies, Hands-on experience with vector databases such as pgvector, FAISS, Pinecone, or Weaviate, Good understanding of embeddings, semantic search, and information retrieval concepts, Experience with MCP or similar tool-integration approaches for AI systems, Experience building and consuming APIs for system integration, Hands-on experience deploying and operating AI/ML services in at least one major cloud platform: Azure, AWS, or GCP, Working knowledge of cloud platforms and/or on-premise deployment models, Understanding of logging, monitoring, and observability fundamentals, Good communication skills and ability to work effectively in cross-functional teams
Nice to Have
Experience with deep learning frameworks such as PyTorch or TensorFlow, Familiarity with MLOps tools and practices such as MLflow, Airflow, or Kubeflow, Exposure to AI safety, guardrails, or evaluation frameworks for LLM-based systems, Experience with backend technologies such as Java or Spring, Experience with frontend technologies for AI-enabled applications, Familiarity with real-time or streaming AI/ML systems, Experience working with enterprise data platforms or data engineering pipelines
What You'll Do.
Develop and maintain AI solutions using both Machine Learning and Generative AI approaches
Build and improve LLM-based applications
Implement and optimize RAG pipelines
Work with vector databases
Develop integrations using MCP or similar approaches
Contribute to API development and integration of AI capabilities
and optimization of AI services
Implement basic guardrails
and monitoring mechanisms
and support AI workloads in Azure
How You'll Work.
Team & Collaboration
Collaborate with cross-functional teams including software engineers, data scientists, and business stakeholders; Contribute to knowledge sharing within the team
Communication Scope
Good communication skills; Ability to work effectively in cross-functional teams
Full Job Description
Company 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
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