AI Engineer at Accesa RaRo

, , Romania -

Full Time

Start Date

Immediate

Expiry Date

02 Aug, 26

Salary

0.0

Posted On

04 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine Learning, Generative AI, LLM Integration, Prompt Engineering, RAG Pipelines, Vector Databases, LangChain, API Development, Azure, AWS, GCP, Pandas, NumPy, Scikit-learn, MCP

Industry

IT Services and IT Consulting

Description

Company 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. Job Description 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 optimize RAG 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 to API development and integration of AI capabilities into existing applications and workflows. Support deployment, monitoring, and optimization of AI services in cloud and/or on-premise environments. Implement basic guardrails, validation, and monitoring mechanisms to improve quality, reliability, and safe usage of AI systems. Deploy, configure, and support AI workloads in Azure, AWS, or GCP, using cloud services for compute, storage, networking, security, and monitoring. Collaborate with cross-functional teams including software engineers, data scientists, and business stakeholders to deliver production-ready solutions. Stay up to date with relevant developments in AI, ML, and GenAI, and contribute to knowledge sharing within the team. Qualifications Must have: 2 - 4 years of experience in Python development, including libraries such as Pandas, NumPy, and Scikit-learn. 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. Additional Information At Accesa you can Enjoy our holistic benefits program that covers the four pillars that we believe come together to support our wellbeing, covering social, physical, emotional wellbeing, as well as work-life fusion. Physical Wellbeing: Our wellbeing program includes medical benefits, gym support, and personalised fitness options for an active lifestyle, complemented by team events and the Healthy Habits Club. Work-Life Fusion: In very dynamic industries such as IT, the line between our professional and personal lives can quickly become blurred. Having a one-size-fits-one approach gives us the flexibility to define the work-life dynamic that works for us. Emotional Wellbeing: We believe that to maintain our overall health, we need to invest in our mental wellbeing just as much as we do in our physical health, social connections or in achieving work-life balance. Social Wellbeing: As a growing community in a hybrid environment, we want to ensure we remain connected not just by the great work we do every day but through our passions and interests. Department: Core Technologies | Data & AI

Responsibilities

Develop and maintain production-ready AI solutions focusing on Machine Learning and Generative AI use cases. This includes building LLM-powered applications, optimizing RAG pipelines, and integrating AI capabilities into enterprise systems.