AI Engineer-Romania

We are looking for an experienced AI Engineer to join our team and contribute to the design, development, and deployment of intelligent AI-powered solutions.
The ideal candidate combines strong software engineering skills with practical experience in Artificial Intelligence, Machine Learning, Generative AI, and Large Language Models (LLMs). You will work closely with Data Scientists, Software Engineers, Product Owners, and business stakeholders to transform business requirements into scalable and production-ready AI solutions.
Responsibilities
Design, develop, and deploy AI and Machine Learning solutions for real-world business use cases.
Develop and integrate Generative AI and LLM-based applications into existing products and platforms.
Build AI-powered applications using techniques such as RAG (Retrieval-Augmented Generation), prompt engineering, embeddings, and vector search.
Develop and maintain scalable data and AI pipelines supporting model training, evaluation, and inference.
Integrate AI models and services into applications through APIs and microservices.
Evaluate and select appropriate AI/ML models, frameworks, and cloud services based on business and technical requirements.
Fine-tune or adapt AI models when required to improve performance for specific use cases.
Implement model evaluation and validation processes to measure accuracy, relevance, robustness, latency, and reliability.
Develop solutions that address AI-specific challenges such as hallucinations, bias, prompt injection, data privacy, and model limitations.
Implement appropriate guardrails, validation mechanisms, and monitoring for AI-powered applications.
Work with structured and unstructured data from multiple sources to support AI use cases.
Collaborate with Data Scientists and Data Engineers to prepare and optimise datasets for AI/ML applications.
Deploy and operate AI solutions in cloud environments and ensure scalability, availability, and performance.
Monitor AI applications and models in production and investigate performance degradation or unexpected behaviour.
Contribute to MLOps/LLMOps practices, including model versioning, deployment automation, monitoring, and lifecycle management.
Write clean, maintainable, and well-tested production code.
Participate in technical discussions, architecture reviews, and code reviews.
Stay up to date with the latest developments in AI, Generative AI, LLMs, and machine learning technologies.
Leverage AI-assisted development tools to improve engineering productivity, automate repetitive tasks, and accelerate solution delivery.
RequirementsMust-Have Skills
3+ years of professional experience in AI Engineering, Machine Learning Engineering, Software Engineering with AI focus, or a related field.
Strong programming skills in Python.
Solid understanding of Machine Learning concepts, algorithms, and model evaluation.
Practical experience developing and deploying AI/ML solutions.
Experience working with Generative AI and Large Language Models (LLMs).
Hands-on experience with RAG architectures, embeddings, vector search, and semantic retrieval.
Experience working with AI/ML frameworks such as PyTorch, TensorFlow, Scikit-learn, or equivalent.
Experience working with LLM APIs and/or open-source models.
Strong understanding of REST APIs, microservices, and software development principles.
Experience with relational and/or NoSQL databases.
Familiarity with vector databases or vector search technologies such as Pinecone, Weaviate, Qdrant, Milvus, Elasticsearch, or similar.
Experience with Git and modern software development practices.
Good understanding of cloud platforms such as Azure, AWS, or GCP.
Familiarity with containerisation technologies such as Docker.
Strong analytical and problem-solving skills.
Good English communication skills.
Nice to Have
Experience with Azure OpenAI, AWS Bedrock, Google Vertex AI, or similar managed AI platforms.
Experience with LangChain, LlamaIndex, Semantic Kernel, or similar AI application frameworks.
Knowledge of MLOps/LLMOps practices and tools.
Experience with Kubernetes and cloud-native architectures.
Experience with MLflow, Kubeflow, or similar ML lifecycle platforms.
Knowledge of AI agents and agentic architectures.
Experience with function calling, tool use, MCP, or multi-agent systems.
Knowledge of prompt engineering and advanced LLM evaluation techniques.
Experience implementing AI safety, governance, security, and responsible AI practices.
Familiarity with monitoring and observability tools such as Prometheus, Grafana, OpenTelemetry, or equivalent.
Experience optimising AI systems for latency, scalability, and cost efficiency.
Experience with fine-tuning, LoRA/PEFT, or other model adaptation techniques.
Knowledge of NLP, computer vision, speech processing, or other specialised AI domains.
Relevant certifications in AI, Machine Learning, or Cloud technologies.

Work arrangement
Yes