Generative AI Specialist

Experis to światowy lider rekrutacji specjalistów i kadry zarządzającej w kluczowych obszarach IT. Z nami znajdziesz konkurencyjne oferty zatrudnienia oraz ciekawe projekty IT skierowane zarówno do ekspertów z wieloletnim doświadczeniem, jak i osób, które dopiero zaczynają swoją przygodę w branży IT.

• Bachelor’s degree in Computer Science, Data Science, Engineering, or equivalent experience.
• 3–5+ years building AI/ML or NLP systems; 1–3+ years hands-on experience with LLMs and generative AI.
• Proficiency in Python or TypeScript/JavaScript; experience with model APIs (OpenAI, Azure OpenAI, AWS Bedrock), vector databases (Pinecone, FAISS, Chroma, Weaviate), and cloud platforms (Azure, AWS, GCP).
• Practical knowledge of RAG, embeddings, prompt engineering, and evaluation frameworks.
• Experience integrating AI into applications such as web, mobile, chat, or agent frameworks.

Nice to have:

• Fine-tuning/instruction tuning experience; familiarity with transformers and modern LLM stacks.
• Knowledge of data governance, privacy, Responsible AI, and security best practices.
• Experience with MLOps tools (MLflow, DVC), feature stores, and observability tools (LangSmith, Helicone, Arize).
• Domain experience in areas like customer support, knowledge management, content generation, analytics, or software development productivity.

• Translate business problems into AI use cases and develop architectures leveraging LLMs, embeddings, retrieval-augmented generation (RAG), and prompt engineering.
• Evaluate and integrate foundation models such as GPT-family, Claude, Llama, and Mistral via APIs or on-prem frameworks; fine-tune or instruct models when appropriate.
• Implement vector stores, index enterprise content, and establish guardrails, prompt templates, and evaluation loops for trustworthy outputs.
• Establish offline and online evaluations (accuracy, latency, toxicity, bias) and apply content filtering, governance, and safety policies.
• Instrument usage analytics, prompt/version management, model monitoring, and cost tracking; automate deployments via CI/CD.
• Collaborate cross-functionally with Product, Legal, Security, and Data teams to prioritize features and meet compliance requirements.
• Create reusable prompts/components, patterns, and developer guides; support internal training and adoption.