Expert AI/ML Engineer

Role & responsibilities

• Professional Background: 6+ years in software, data, or AI engineering, featuring at least 34 years of direct experience architecting and implementing AI/ML solutions.
• Academic Foundation: BSc, MSc, or PhD in Computer Science, Mathematics, or a related quantitative field, with a deep command of probability, statistics, and machine learning optimization.
• AI & Generative AI Expertise: Proven track record in building and deploying advanced AI systems, including Large Language Models (LLMs), Multimodal architectures, RAG, and Agentic systems.
• Engineering Proficiency: Expert-level Python skills and mastery of frameworks such as PyTorch, TensorFlow, LangChain, and Hugging Face.
• Cloud & Infrastructure: Hands-on experience with cloud-native AI stacks (AWS SageMaker, Azure ML, or GCP Vertex AI) and enterprise data platforms.
• Operational Excellence (LLMOps): Proficiency in modern AI engineering practices, including CI/CD, model versioning, observability, and evaluation methodologies (e.g., CRISP-ML(Q)).
• Full-Stack AI Delivery: Experience architecting end-to-end pipelines—from data ingestion and API integration to production-grade model serving and optimization.
• Data Sophistication: Ability to handle diverse modalities (text, image, audio) and complex scenarios such as time-series forecasting and anomaly detection.
• Governance & Security: Strong understanding of security, data privacy (GDPR/CCPA), and ethical AI frameworks within enterprise system design.

Preferred candidate profile

Perks and benefits