Senior AI​/ML Consultant

The role

As a Senior AI/ML Consultant, you are the person clients trust to turn AI ambition into production reality. You design and build AI systems that hold up beyond the demo. Machine learning pipelines, generative AI solutions, and the technical foundations that let organisations extract real value from AI.

Our consultants bring varied paths and perspectives to this work. What unites them is a principled approach to system design, a genuine curiosity about the business problems behind each model, seeing work through from the first experiment to a system running in production. Across engagements, you may work as an ML engineer, AI architect, LLM/GenAI specialist, or a strategic advisor, often a combination of several within one engagement.

Building AI systems, shaping strategy. You design, build, and ope rationalise AI systems across the full stack, from data pipelines and feature engineering to LLM applications, RAG pipelines, and agentic workflows. You use MLOps practices to bring models into production reliably and keep them there. And you work with clients to make the decisions that matter: build versus buy, model selection, responsible AI, and the organisational change that turns AI from a pilot into a practice.

Delivering, leading, growing. You are growing as a consultant and a leader, not just delivering on the technical side. As a senior consultant, you are expected to take ownership beyond your immediate delivery: guiding junior colleagues, contributing to engagement quality, and beginning to build the presence and trust that defines the next stage of your career. Beyond client projects, you are will to contribute to Netlight's thought leadership within Data & AI, whether through internal knowledge sharing, external publications, or coaching colleagues who are earlier in their journey.

• Possibility to be part of growing one of the most successful tech consultancies in the industry.
• Diverse client landscape, including some of the most innovative tech companies & exciting projects with lots of room for professional development.
• Access to an international network of 2,000+ highly talented individuals.
• An experienced mentor and a professional coach to guide you throughout your career.
• An international & diverse working environment with a central and modern office space.
• Competitive and transparent salary model with equal pay regardless of the assignment, biannual salary reviews, and attractive benefits (e.g. company shares, Deutschlandticket, parental benefits & much more).
• The freedom to shape your own path in terms of clients, domains, and roles, supported by a global community.
• Access to Netlight's Edge network with its competence cells, conferences, and a culture where sharing knowledge is as valued as acquiring it. You are encouraged to contribute to Netlight's thought leadership within Data & AI.

Master's Degree in Engineering or Technology (e.g., Computer Science, Machine Learning, Mathematics, Statistics, or similar).

Verbal and written fluency in English.

2–4 years of full-time work experience in machine learning engineering, applied AI, or a closely related field. Experience working in agile product or delivery teams, gained after completing your Master's degree.

Strong hands‑on experience with the full ML lifecycle: data preparation, feature engineering, model development, evaluation, deployment, and monitoring.

Practical experience building and deploying solutions on at least one major cloud platform (AWS, Azure, or GCP), including managed ML services (e.g., Sage Maker, Vertex AI, Azure ML).

Hands‑on experience with LLMs and generative AI, including prompt engineering, RAG architectures, and integrating foundation model APIs into production systems.

Experience with MLOps practices and tools, including experiment tracking, model registries, CI/CD for ML, and model serving infrastructure.

Proficiency in Python and relevant ML/AI frameworks (e.g., PyTorch, Tensor Flow, Hugging Face, Lang Chain, or Llama Index).

Demonstrated ability to collaborate with diverse stakeholders — including data engineers, product owners, analysts, and business leaders — in…