Engineering Manager - Applied Ai

Technical Experience

Deep expertise in machine learning, generative AI, deep learning, NLP, recommendation systems, and data mining.
Hands-on knowledge of ML and GenAI frameworks: Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers, and LLM fine-tuning.
Proficiency with the OpenAI SDK and familiarity with major LLM provider APIs for building and orchestrating production AI systems.
Hands-on experience with LangGraph for building stateful, multi-step agentic workflows and complex agent orchestration patterns.
Experience with Hugging Face (Transformers, Hub, Inference API) for model sourcing, fine-tuning, and deployment.
Solid understanding of embeddings, dense and sparse retrieval, vector databases, and semantic search, for building RAG pipelines and similarity-based applications.
Strong software engineering fundamentals: clean production code, data structures and algorithms, ML system design.
Proven experience shipping and monitoring ML models in production, with a solid grasp of MLOps practices.
Strong data and ML engineering skills: building and orchestrating data pipelines (e.g., Airflow) and robust feature engineering.
Excellent SQL and Python; solid statistical foundations including experiment design, causal inference, and predictive methods.
Familiarity with agentic system design (harness-first thinking, LLM observability, evaluation pipelines, and production feedback loops) is a strong plus.
Experience with BigQuery and Google Cloud Platform is a plus.

Qualifications

Bachelor's degree in Engineering, Computer Science, or a related field. A postgraduate degree is a plus.
6+ years of experience in data science, ML engineering, and/or generative AI, including shipping models to production.
2+ years managing or leading a data science or ML team, with a track record of developing talent and delivering through others.
Experience building ML systems in an online consumer product environment is a strong plus.