AI Engineering Lead

📋 Description

• Lead end-to-end project delivery with governance and stakeholder communication
• Mentor junior engineers and contribute to proposals
• Design RAG systems, agentic frameworks, and LLM-powered solutions for production
• Automate the full MLOps/LLMOps lifecycle including tracking, deployment, monitoring
• Design APIs, microservices, and orchestration layers focused on latency and cost
• Communicate risks and tradeoffs transparently to clients

🎯 Requirements

• Expert-level Python, strong Git, and ML/LLM versioning
• Cloud experience across AWS, Azure, or GCP (Azure preferred); containerization and orchestration
• Hands-on RAG experience with embeddings, retrieval, and evaluation
• Proven MLOps/LLMOps track record (MLflow, Weights & Biases, or similar)
• Experience with event-driven architectures, APIs, and microservices
• Strong communication skills to engage engineering teams and senior stakeholders

🎁 Benefits

• Learning opportunities: certifications in AWS, Databricks, Snowflake
• Access to AI learning paths and Udemy Business
• English lessons and professional development programs
• Travel opportunities to attend industry conferences
• Mentoring and career development plans
• Flexible working options and statutory benefits