AI Engineering Lead

📋 Description

• Lead end-to-end project delivery with governance and stakeholders
• Mentor engineers and contribute to proposals/new business
• Define AI scope, communicate risks and tradeoffs
• Design and build RAG systems and LLM-powered solutions
• Apply prompt engineering, few-shot sets, tool prompts
• Lead feasibility: prompting vs RAG vs fine-tuning vs classical ML

🎯 Requirements

• Expert-level Python, Git, ML/LLM versioning
• Cloud: AWS/Azure/GCP; containerization & orchestration
• Hands-on RAG: embeddings, retrieval, evaluation
• MLOps/LLMOps: MLflow, Weights & Biases, similar tools
• Experiment design: metrics, data curation, structured tests
• Event-driven APIs/microservices; strong communication

🎁 Benefits

• Certifications in AWS, Databricks, Snowflake
• AI learning paths and Udemy Business access
• Mentorship and career development programs
• Company equipment and flexible working options