What you will do

• Design, develop, and deploy Generative AI applications using AWS Bedrock, SageMaker, and open-source LLMs.
• Create and optimize prompting strategies, system prompts, guardrails, and evaluation frameworks for LLM applications.
• Build RAG pipelines using Bedrock Knowledge Bases, vector stores, or external solutions.
• Fine-tune foundation models using SageMaker or Databricks MLflow.
• Work with structured and unstructured data (text, documents, logs) to support GenAI use cases.
• Implement ML pipelines, including data ingestion, feature engineering, model training, evaluation, and deployment.
• Integrate ML/LLM services with backend systems, APIs, Snowflake data products, and Databricks workflows.
• Build CI/CD pipelines for ML/GenAI workloads.
• Monitor model performance, detect data/model drift, and automate retraining.
• Collaborate with product owners, cloud engineers, data engineers, and architects to translate business requirements into GenAI solutions.
• Participate in client workshops, POVs/POCs, and design sessions for new AI/ML use cases.
• Produce architecture diagrams, technical documentation, and best-practice guidelines.
• Apply responsible AI principles in all solutions, ensuring secure data handling and compliance with enterprise standards.
• Explore and benchmark emerging LLMs, embeddings models, and vector database solutions across AWS, Snowflake, and Databricks ecosystems.
• Share knowledge, mentor team members, and contribute to internal accelerators or reusable components.

Flexible: The work location for this role may include a mix of working remotely, onsite at a client, or in an Accenture office—depending on specific project circumstances. With all our roles, there is some in-person time for collaboration, learning, and building relationships with clients, peers, leaders, and communities. As an employer, we will be as flexible as possible to support your specific work/life needs.

What we offer

• Permanent employment contract.
• Individual support of a People Lead and a clear path of professional development, including the possibility of a session with a Coach.
• A wide training package: soft skills, technical, and language training; access to e-learning platforms; Gallup test; GenAI training; and the possibility of co-financing courses and certifications.
• Employee Assistance Program – legal, financial, and psychological consultations.
• Employee share purchase plan – eligible employees automatically become eligible for quarterly dividends if they own company shares.
• Paid employee referral program.
• Private medical care and life insurance.
• Access to the benefit platform (including the Multisport card and a wide range of products and services).

Requirements

• Strong experience designing, developing, and deploying Generative AI applications using AWS Bedrock, SageMaker, and open-source LLMs.
• Proficiency in prompt engineering, guardrails, evaluation frameworks, and building RAG pipelines with vector databases or Bedrock Knowledge Bases.
• Hands-on skills in fine-tuning models, implementing ML pipelines, and working with structured/unstructured data across AWS, Snowflake, and Databricks.
• Advanced Python programming skills, including experience with ML/AI Python libraries.
• Solid understanding of MLOps practices: monitoring, drift detection, automated retraining, and lifecycle management.
• Competence in integrating ML/LLM services with backend systems, data platforms, APIs, and enterprise environments.
• Strong collaboration, communication, and documentation skills, with a commitment to responsible AI and secure data handling.

BONUS POINTS IF YOU HAVE:

• Certifications in AWS, Snowflake, or Databricks, or hands-on experience with emerging GenAI tooling.

Research indicates that some candidates, especially the most diverse ones, may hesitate to apply for positions if they don't meet all requirements. If you believe you possess the necessary skills, even if not meeting every requirement, we wholeheartedly encourage you to submit your application.