GenAI Engineer (Mid) (AI&Data)

What you will do

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.

THE WORK

As a GenAI Engineer, you will independently design, build, and deliver production-ready GenAI and agentic AI solutions for leading global enterprises. We’re looking for engineers who combine solid software development fundamentals with real, hands-on experience in the GenAI space – people who can take a problem, pick the right approach, and ship it to production.

You will:

• Design and build GenAI applications and multi-agent systems for enterprise clients – using Python, LLM APIs (OpenAI, Anthropic, Google), and open-source models
• Develop RAG pipelines, agentic workflows, and integrations that connect AI agents to corporate databases, APIs, and enterprise systems
• Take ownership of features end-to-end: from prototyping and evaluation through deployment to cloud environments (AWS, Azure, or GCP) with production-grade reliability
• Evaluate and optimize AI outputs – improving accuracy, reducing hallucinations, and tuning performance for real-world use cases
• Collaborate with senior engineers, architects, and consultants on high-impact client projects, contributing to design decisions and technical direction

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.

What we offer

• 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)

Interview Note: We value practical skills. During the technical interview, we’ll give you a real problem to solve and debug together – using your preferred development setup, including any AI-assisted tools you work with day to day.

Requirements

• Min. 3 years of commercial experience in Software Engineering or Data Engineering, including at least 1 year working hands-on with LLMs and Generative AI
• Strong Python skills and solid understanding of SQL
• Practical experience building RAG pipelines, agentic workflows, or working with vector databases
• Hands-on knowledge of agentic frameworks (e.g., LangChain, LangGraph, LlamaIndex, ADK) and an understanding of agentic architecture patterns
• Experience deploying AI solutions to cloud environments (AWS, Azure, or GCP)
• Understanding of the Model Context Protocol (MCP) and how it connects AI agents to external systems
• Proficiency with AI-assisted development tools (e.g., Cursor, Copilot, or similar) – we expect these to be a core part of your daily workflow, not an occasional add-on
• Good command of English (min. B2+)

BONUS POINTS IF YOU HAVE

• Experience building multi-agent systems or autonomous AI workflows in a commercial environment
• Hands-on experience with vector database optimization, chunking strategies, and embedding tuning
• Familiarity with LLM

Ops practices, model evaluation frameworks, and CI/CD for AI applications

• Experience with containerization and API tooling (Docker, FastAPI, Flask)
• Understanding of Responsible AI principles, hallucination mitigation strategies, and AI Act awareness
• Contributions to open-source projects, hackathon experience, or a public GitHub portfolio
• Experience mentoring junior engineer