Associate Consultant Generative AI

Sia is a next-generation, global management consulting group. Founded in 1999, we were born digital. Today our strategy and management capabilities are augmented by data science, enhanced by creativity and driven by responsibility. We’re optimists for change and we help clients initiate, navigate and benefit from transformation. We believe optimism is a force multiplier, helping clients to mitigate downside and maximize opportunity.

With expertise across a broad range of sectors and services, our 3,000 consultants serve clients worldwide from 48 locations in 19 countries. Our expertise delivers results. Our optimism transforms outcomes.

Sia’s AI & Data Business Unit is the powerhouse of our firm’s innovation—merging cutting‑edge Data Science, Generative AI, and advanced digital solutions to transform industries. With over 350 experts worldwide, we tackle projects from proof‑of‑concept to large‑scale deployment, always pushing the boundaries of AI capabilities. Our 12 R&D labs in Europe and North America drive continuous research in areas like computer vision, MLOps, and deep learning, partnering closely with our business consultants for real‑world impact.

By joining Sia’s AI & Data team, you’ll step into a vibrant, collaborative environment that nurtures professional growth and empowers you to shape the future of AI‑driven consulting.

We are growing our Generative AI consulting practice and looking for motivated recent graduates to join as GenAI Consultants. You’ll work at the intersection of cutting‑edge AI and real business problems — helping clients across industries design, build, and deploy LLM‑powered solutions that create tangible value.

This is a hands‑on technical role. You’ll contribute to the full lifecycle of GenAI projects: from architecture and prototyping through to production deployment, evaluation, and iteration. We invest heavily in your development, and expect you to do the same.

What You’ll Do

You’ll design and implement GenAI/LLM solutions leveraging models such as Claude, GPT, Gemini, Llama, and Mistral — selecting the right approach (RAG, agents, fine‑tuning, prompt engineering) for each client context. You’ll support senior architects in designing scalable, secure, and compliant AI applications while building hands‑on experience across the full stack.

Responsibilities

• LLM/GenAI System Development:
  Design, build, train, fine‑tune, and deploy sophisticated AI models leveraging LLMs (e.g., GPT‑x, Claude, Gemini, Llama, Mistral) and other generative techniques.
• Assist in Solution Architecture:
  Support the GenAI Solution Architect in designing robust, scalable, and secure applications.
• Application Development:
  Develop applications powered by GenAI models (both self‑managed and API‑accessible) that meet business needs and comply with applicable regulations (GDPR, EU AI Act, model licenses, etc.).
• Advanced Prompt Engineering:
  Design and optimize effective prompts (e.g., few‑shot, Chain/Tree/Graph of Thought, ReAct, Self‑reflection, guardrails), balancing simplicity and complexity to enhance analytical capabilities, refine outputs, improve user experience, and control interactions.
• RAG Implementation:
  Design and implement Retrieval‑Augmented Generation (RAG) architectures to improve accuracy and relevance by retrieving information from pre‑determined knowledge sources, providing traceability (source attribution).
• Model Selection & Fine‑Tuning:
  Select and fine‑tune appropriate models (including multimodal - VLM, SLM - Visual Language Models, Small Language Models) to create higher‑quality content (text, image, audio, code, etc.) and maximize business value creation opportunities.
• Integration & Deployment (MLOps):
  Implement MLOps best practices for the GenAI lifecycle, including automated pipelines (CI/CD), versioning, monitoring, and maintenance in production environments (Cloud platforms like AWS, Azure, GCP). Ensure seamless integration into existing systems and with external tools/APIs, potentially utilizing standardized protocols (MCP).
• Evaluation & Responsible AI:
  Develop and execute rigorous evaluation frameworks to measure model performance, reliability, fairness,…