Artificial Intelligence Specialist

30 ago - Roma
Hermes Corporate

At Hermes Corporate, we have been supporting companies in digital transformation for over 15 years through IT consulting, outsourcing services, and digital talent development.
Through our WINGS 2 FLY consulting services, we promote a modern digital culture by helping organizations grow with skilled professionals, dedicated teams, and innovative digital solutions.
We are currently looking for an AI Specialist with strong expertise in Large Language Models (LLMs), applied AI research, and rapid prototyping. In this role, you will focus on developing, fine-tuning, and evaluating AI/ML models while exploring innovative approaches in LLMs, RAG architectures, and agentic systems.
You will work hands-on with open-source models such as LLaMA, Mistral, and Falcon, building proofs of concept (POCs) and validating AI use cases before collaborating with AI Engineers to transition successful solutions into production environments.
Key Responsibilities: Develop, train, and evaluate AI/ML models, with a strong emphasis on LLMs
Work with open-source LLMs such as LLaMA, Mistral, and Falcon




Design and build proofs of concept (POCs) to validate AI use cases and technical approaches
Fine-tune models for domain-specific applications
Conduct applied research on LLMs, RAG, and agentic systems
Analyze model performance and propose improvements
Stay up to date with advancements in AI and machine learning
Collaborate with AI Engineers to support productionization efforts
Required Qualifications: 3+ years of experience in developing AI solutions
Master’s degree or PhD in AI, Machine Learning, or related field
Strong foundation in machine learning and deep learning
Proficiency in Python and frameworks such as PyTorch or TensorFlow
Hands-on experience with transformer architectures and LLMs
Experience with fine-tuning, embeddings, and model evaluation
Ability to rapidly prototype and iterate on ideas
Strong analytical and research skills
Preferred Qualifications: Experience with model optimizat

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