About the role
As a Senior Member of the AI team, you will drive architectural decisions, establish best practices, and deliver robust, scalable AI systems, with a strong focus on AI-powered and agentic applications in production environments.
Responsibilities:
- Lead AI Development: Own the end-to-end design, development, and operation of production-grade AI/ML solutions, including ML and NLP models, LLMs, RAG, and multi-agent systems. Make key architectural decisions across model selection, training strategies, retrieval mechanisms, orchestration layers, and infrastructure.
- AI done right: Create evaluations and define metrics of success using cutting-edge AI platforms.
- MLOps: Implement and manage MLOps practices to streamline the deployment, monitoring, and maintenance of models in production environments.
- Collaboration and Leadership: Work closely with software engineers, data scientists, and product teams to align AI capabilities with business needs.
- Mentorship and Coaching: Guide and support more junior AI engineers, fostering a culture of learning and knowledge sharing.
Requirements:
- BSc/Master's degree in Computer Science, Machine Learning, Data Science, or a related field.
- Hands-on experience in LLM-powered production applications or ML/NLP applications.
- Strong proficiency in Python and AI frameworks.
- Strong understanding of machine learning fundamentals (supervised/unsupervised learning, optimization, model evaluation).
- Solid experience with NLP systems (text classification, embeddings, retrieval systems, semantic search, etc. ).
- Hands-on MLOps experience: reproducible pipelines, experiment tracking, automated evaluation, monitoring, and CI/CD for models and AI artifacts.
- Solid understanding of AI model fine-tuning, optimization, and evaluation.
- Proven experience with AWS and cloud-based AI deployments.
- Strong communication skills in English, capable of explaining complex AI concepts to technical and non-technical stakeholders; we believe in learning by sharing.
- Knowledge of reinforcement learning, retrieval-augmented generation (RAG), and multi-agent AI architectures.
- Strong data intuition: ability to inspect raw logs, design metrics, and quickly identify quality regressions.
- Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.
Strong Plus:
- Strong AWS (or equivalent cloud platform) experience for scalable AI infrastructure.
- Experience optimizing models for latency, throughput, and cost.
- Experience fine-tuning large language models.
- Familiarity with multi-agent systems and orchestration frameworks.
- Experience designing AI systems in enterprise or B2B environments.