Senior Data Scientist – LLM Expert

Kyndryl; Granada; Presencial; EUR 70.000 - 90.000; Jornada completa

Descripción de la vacante

A leading technology services company in Spain is looking for a Senior Data Scientist specialized in Large Language Models. You will work on designing, fine-tuning, and deploying LLM-based solutions. The ideal candidate has over 4 years of AI development experience, a strong background in LLMs, and a hands-on approach to technology. This role offers the opportunity to collaborate with top global clients and push the boundaries of generative AI.

Servicios

Access to top learning programs

Volunteering and giving opportunities

Diverse workplace culture

Formación

• 4+ years of experience in AI model development, with 2 years on LLMs.
• Proven expertise in fine-tuning and evaluation of foundation models.
• Hands-on experience developing RAG pipelines.

Responsabilidades

• Design, train, and fine-tune large language models.
• Develop Generative AI solutions combining reasoning and language understanding.
• Collaborate with architects and ML engineers to bring solutions into production.

Conocimientos

AI model development

Large Language Models

fine-tuning

NLP systems

Python

RAG pipelines

Responsible AI

collaboration

Educación

Bachelor's or Master's degree in Computer Engineering, Data Science, or related field

Postgraduate studies in NLP, Deep Learning, or AI

Herramientas

PyTorch

MLflow

Vertex AI

Descripción del empleo

Description

As a Senior Data Scientist specialized in Large Language Models at Kyndryl’s AI Innovation Hub, you’ll help shape the frontier of intelligent and agentic systems — turning cutting‑edge research into real‑world impact for some of the world’s most forward‑thinking enterprises.

You’ll work with state‑of‑the‑art technologies, foundation models, and emerging agentic frameworks to design, fine‑tune, and deploy powerful LLM‑based solutions that understand, reason, and generate language with purpose.

In this role, you’ll collaborate closely with architects, ML engineers, and product teams to transform ideas into production‑ready systems that redefine how organizations interact with knowledge and automation.

You’ll operate in an environment that values experimentation, precision, and creativity — where you’ll be encouraged to explore the limits of generative AI, drive technical excellence, and contribute to solutions used by top global clients.

If you’re passionate about transforming language models into intelligent agents that can think, act, and assist — this is where your work will make a lasting mark.

Your Mission

• Design, train, fine-tune, and evaluatelarge language modelsand their distilled or specialized variants (SLMs).
• Develop and integrateGenerative AI and Agentic AI solutionsthat combine reasoning, retrieval, and language understanding.
• Implement and optimizeRAG (Retrieval‑Augmented Generation)pipelines, ensuring precision, traceability, and scalability.
• Collaborate with architects and ML engineers to bring LLM‑based solutions into production environments.
• Define and apply robustevaluation frameworksand metrics for alignment, safety, hallucination control, and factual consistency.
• Contribute to the Hub’s innovation roadmap by exploring new models, architectures, and methods for fine‑tuning and instruction learning.
• Advise on model optimization, includingquantization,distillation, andmemory‑efficient inference.
• Promote best practices in documentation, traceability, and Responsible AI across all LLM‑based initiatives.

Who You Are

Essential Qualifications

• 4+ years of experience in AI model development, with at least 2 years focused onLLMs or advanced NLP systems.
• Proven expertise infine‑tuning, instruction tuning, andevaluationof foundation models (GPT, Llama, Claude, Mistral, Gemma, etc.).
• Hands‑on experience developingRAG pipelinesandLLM‑based reasoning architectures.
• Solid understanding ofPythonand major libraries for LLM and AI development (Transformers, LangChain, LlamaIndex, PEFT, BitsAndBytes, LoRA, Accelerate).
• Experience training or serving models onGPU / TPU environments(PyTorch, DeepSpeed, vLLM, Ollama).
• Strong background invector databasesandembedding models.
• Familiarity withmulti‑agent frameworksand orchestration systems (CrewAI, AutoGen, LangGraph, Google ADK).
• KnowledgeofResponsible AI and evaluation principles— safety, bias mitigation, factual alignment, and model interpretability.
• Experience deploying models inMLOps environments(MLflow, Vertex AI, Azure ML, OpenShift AI).

Education & Certifications

• Bachelor’s or Master’s degree inComputer Engineering,Data Science,Mathematics,Computational Linguistics, or related field.
• Postgraduate studies (Master’s or PhD) inNatural Language Processing (NLP),Deep Learning, orArtificial Intelligenceare highly valued.
• Additional training inGenerative AI,Prompt Engineering, orResponsible AIis a plus.
• Demonstrated commitment to continuous learning and staying current with the latest research in LLMs and generative AI.

Preferred Skills

• Expertise inprompt optimization,context management, andhallucination detection / mitigation.
• Understanding ofLLMOpspractices andobservability toolsfor model monitoring and evaluation.
• Experience withdistillation,quantization, andefficient inference strategies for scaling LLMs in production.
• Familiarity withsemantic search,retrieval frameworks, andknowledge‑grounded generation.
• Background inreinforcement learning from human feedback (RLHF)orpreference optimization.
• Ability to analyze research papers, prototype rapidly, and transfer innovation into production‑ready systems.
• Comfort working withhybrid architectures, integrating models across cloud and on‑premise environments.

Soft Skills

• Clear and articulate communicator , able to explain complex model behavior and design choices to diverse audiences.
• Analytical and experimental mindset , combining scientific rigor with creativity and problem‑solving agility.
• Collaborative and cross‑functional approach , working closely with architects, engineers, and designers to deliver cohesive AI solutions.
• Curiosity and innovation‑driven attitude , always exploring new models, datasets, and emerging techniques in the LLM space.
• Business awareness , able to translate functional requirements into measurable AI outcomes.
• Commitment to quality and reproducibility , ensuring experiments, metrics, and models are well‑documented and traceable.
• Adaptability and growth mindset , embracing continuous learning in the rapidly evolving world of generative AI.

#AgenticAI

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