AI Engineer – Generative AI & LLM Solutions

About the role

Accenture Technology is seeking an AI Engineer to design, build, and scale intelligent AI‑powered solutions. The role involves working with large language models, generative AI ecosystems, and modern ML technologies to deliver robust applications for clients in a hybrid Lisbon setting.

Key responsibilities

• Research, design, and develop AI‑powered solutions using state‑of‑the‑art ML and generative AI technologies.
• Build and optimise LLM‑based applications, including Retrieval‑Augmented Generation pipelines and agentic workflows.
• Design and integrate vector databases, prompt‑engineering strategies, and tool‑calling mechanisms.
• Deploy, monitor, and optimise AI systems for performance, reliability, and scalability.
• Apply AI safety, evaluation, and guardrail best practices across solutions.
• Collaborate with cross‑functional teams to translate business needs into AI solutions.
• Contribute to architecture decisions and platform scalability initiatives.
• Document designs, experiments, and operational procedures.
• Depending on seniority, mentor engineers, lead technical initiatives, and drive AI strategy.

Required profile

• Experience working with Large Language Models and generative AI ecosystems.
• Strong proficiency in Python and modern software engineering practices.
• Solid understanding of AI application architectures, including Retrieval‑Augmented Generation.
• Experience with vector databases and semantic search solutions.
• Knowledge of agentic frameworks, tool calling, and orchestration patterns.
• Understanding of AI safety, evaluation, monitoring, and guardrails.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Familiarity with MLOps practices, CI/CD pipelines, and model deployment workflows.
• Strong analytical, problem‑solving, and communication skills.

Required skills

• Python
• Large Language Models (LLMs)
• Generative AI ecosystems
• Retrieval‑Augmented Generation (RAG)
• Vector databases
• Semantic search
• Agentic frameworks and tool‑calling
• AI safety and evaluation
• AWS, Azure, GCP
• MLOps, CI/CD pipelines, model deployment
• LangChain, LlamaIndex, Haystack (nice‑to‑have)