ML/AI Engineer with Snowflake (AI&Data)

At the AI&Data, we work locally and globally with over 250 Poland-based specialists. Our expertise spans data architecture and sectoral specialization. We collaborate with tech partners like Microsoft, AWS, Google, Oracle, and Snowflake to implement cutting-edge solutions. We provide comprehensive support from strategy definition to solution implementation and development, covering all aspects of Data & Analytics.

You will:

• design and develop GenAI-powered solutions supporting business processes and decision-making across various functional areas.
• collaborate with business stakeholders, architects and engineers to identify practical GenAI use cases and translate them into scalable solutions.
• build and integrate applications leveraging LLMs with cloud AI services (e.g. Snowflake Cortex, AWS Bedrock and other).
• design and implement retrieval-augmented generation (RAG/GraphRAG) pipelines, combining enterprise data with GenAI models.
• work with structured and unstructured data, preparing and optimizing pipelines for GenAI use cases.
• contribute to both high-level solution design and hands-on implementation, ensuring solutions are scalable, secure and maintainable.
• experiment with prompt engineering, context engineering, agentic orchestration, embeddings and model configurations to improve solution quality.
• support POC/POV initiatives, validating GenAI technologies and identifying their business value.
• collaborate with data engineering teams to integrate GenAI solutions into modern data platforms.
• stay up to date with evolving GenAI trends and contribute to internal knowledge sharing and capability building.

Flexible: The work location for this role may include a mix of working remotely (most of the time), 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.

• A permanent employment contract
• Individual support of a People Lead and a clearly defined path of professional development, including the possibility of a session with a Coach
• A comprehensive training package, including: soft skills, technical and language training, access to e-learning platforms, Gallup test, GenAI training, possibility of co-financing courses and certifications
• Employee Assistance Program offering legal, financial and psychological consultations
• Employee Share Purchase Plan – employees who own company shares are automatically eligible for quarterly dividends
• Paid employee referral program
• Private medical care and life insurance
• Access to the Worksmile benefits platform (including the Multisport card)

• Experience with Python and ability to build data/AI applications.
• Understanding of GenAI/LLM concepts (e.g., context engineering, prompt engineering, embeddings, RAG) and eagerness to deepen knowledge in this area.
• Familiarity with Snowflake or other modern data platforms, or willingness to develop in this area.
• Experience with at least one cloud platform (AWS, Azure, or GCP).
• Ability to work with APIs and integrate external AI services into applications.
• Experience in working with APIs and integration of external AI services into applications
• Understanding of data engineering fundamentals (data pipelines, data transformation, working with large datasets).
• Ability to combine architectural thinking with hands-on implementation.
• Strong problem-solving skills and ability to work in a team environment.
• Interest in building production-ready solutions (not only experiments or prototypes).
• Excellent command of spoken and written English (B2 level).

BONUS POINTS IF YOU HAVE

• Experience with frameworks such as LangChain, LlamaIndex or similar.
• Familiarity with vector databases and semantic search solutions.
• Exposure to Snowflake Cortex or similar platform-native GenAI capabilities.
• Experience with building RAG-based solutions or LLM integrations.
• Knowledge of MLOps / LLMOps practices (CI/CD, evaluation, monitoring).
• Experience with tools such as DBT, Airflow or other data pipeline solutions.
• Familiarity with containerization (Docker, Kubernetes) and infrastructure as code (Terraform).
• Basic understanding of AI governance, security and responsible AI practices.
• Experience working in Agile delivery models.