We are tech transformation specialists, uniting human expertise with AI to create scalable tech solutions.
With over 8,000 CI&Ters around the world, we’ve built partnerships with more than 1,000 clients during our 30 years of history. Artificial Intelligence is our reality.
We are looking for an AI Engineer to join our global team and help design, build, and deploy intelligent solutions that leverage Machine Learning and Generative AI technologies. In this role, you will work closely with cross-functional teams including product, data, and engineering to transform business challenges into scalable AI-powered solutions.
Main Responsibilities
- Design, develop, and deploy AI-driven applications and services, with a strong focus on Machine Learning and Generative AI use cases.
- Build and integrate LLM-based solutions using frameworks such as LangChain , ensuring reliability, scalability, and performance.
- Collaborate with product managers, designers, and data teams to translate business requirements into technical AI solutions.
- Implement data pipelines, model training workflows, and inference services across different environments.
- Optimize models and systems for performance, cost, and scalability in production.
- Ensure best practices related to model evaluation, monitoring, versioning, and responsible AI usage.
- Contribute to technical discussions, architectural decisions, and proof-of-concepts for AI initiatives.
- Stay up to date with emerging AI technologies, tools, and industry trends.
Requirements
- Strong experience as an AI Engineer, Machine Learning Engineer, or similar role.
- Solid knowledge of Machine Learning concepts, algorithms, and model lifecycle.
- Hands-on experience with Generative AI (GenAI) , including LLMs and prompt engineering.
- Experience using LangChain to build and orchestrate LLM-based applications.
- Proficiency in Python and common AI/ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Experience deploying AI solutions in cloud environments (AWS, Azure, or GCP).
- Understanding of software engineering best practices, including version control, testing, and CI/CD.
- Advanced English (spoken and written), with the ability to communicate effectively with technical and non-technical stakeholders.
Nice to Have
- Experience with MLOps practices, including model monitoring, retraining, and automation.
- Familiarity with vector databases, embeddings, and retrieval-augmented generation (RAG) patterns.
- Knowledge of data engineering concepts and tools.
- Experience working in agile environments and cross-functional teams.
- Exposure to AI ethics, privacy, and security best practices.
- Previous experience in consultancy or client-facing projects.