Lead AI Engineer

Your tasks

Lead the end-to-end technical implementation of AI/ML projects, from prototyping and experimentation to production deployment and optimization.

Design and review the architecture of AI systems, including data pipelines, model serving, vector databases, RAG systems, and agentic workflows.

Guide the development of classical ML models (prediction, ranking, recommendation, scoring) and GenAI solutions (RAG, AI agents, LLM applications).

Evaluate and select appropriate approaches for each problem, including classical ML, LLMs, fine-tuning, or hybrid solutions, based on accuracy, cost, performance, and maintainability.

Provide hands-on technical guidance, conduct code and architecture reviews, and mentor AI engineers.

Implement and maintain engineering best practices for code quality, testing, CI/CD, MLOps, model evaluation, and monitoring.

Ensure production AI systems are reliable, scalable, secure, and cost-efficient, applying appropriate safeguards for data privacy and responsible AI.

Collaborate with product, data, and engineering teams to translate business requirements into technical solutions and communicate technical trade-offs to stakeholders.

Troubleshoot complex technical challenges, optimize system performance, and contribute to the development of reusable AI components.

Requirements

Bachelor’s or Master’s in Computer Science, Engineering, or a related field.

6+ years in AI, ML, or data science roles.

1+ years leading a team of engineers, including hiring and people management.

Proven track record building and deploying classical ML models (e.g., gradient boosting, regression, classification, recommender systems, time-series forecasting).

Strong grasp of feature engineering, model validation, and experiment design (A/B testing).

Proven experience designing and deploying GenAI systems using LLMs.

Hands-on experience with RAG, vector databases, and embedding models.

Experience with agent frameworks such as LangChain, LangGraph, or PydanticAI.

Experience with LLM evaluation, observability, and cost optimization.

Expert in Python and ML frameworks (scikit-learn, XGBoost/LightGBM, PyTorch).

Strong MLOps experience: model versioning, CI/CD for ML, monitoring, and drift detection.

Hands-on experience with a major cloud platform (AWS, Azure, or GCP).

Strong communication and stakeholder management skills.

Experience with multi-agent systems or tool-using LLMs.

Hands-on experience fine-tuning LLMs, including parameter-efficient techniques such as LoRA or QLoRA.

Contributions to open-source AI/ML projects or experience conducting applied research in machine learning, NLP, or LLMs.