Design, build, and deploy production GenAI systems, including LLM applications, agentic workflows, RAG pipelines, and AI-powered search capabilities.
Architect scalable AI services using modern ML frameworks, model-serving tools, APIs, Docker, Kubernetes, and CI/CD pipelines.
Develop and optimize retrieval systems using embeddings, vector databases, semantic search, reranking, and structured data sources.
Fine-tune, adapt, and evaluate LLMs for domain-specific use cases using prompt engineering, supervised fine-tuning, LoRA / QLoRA, or related methods.
Build automated evaluation frameworks to measure model quality, prompt performance, retrieval accuracy, reasoning reliability, latency, and cost.
Implement observability for AI systems, including tracing, logging, performance monitoring, drift detection, and output-quality review.
Translate prototypes and research concepts into reliable product features that can scale in production.
Partner with product managers, data engineers, backend engineers, analysts, and business stakeholders to define AI capabilities and technical tradeoffs.
Review architecture, provide technical guidance, mentor junior team members, and promote strong engineering practices.
Create clear technical documentation, implementation plans, runbooks, and model lifecycle documentation.

5+ years of experience in machine learning engineering, AI engineering, data science engineering, or a related technical role.
2+ years of experience building or shipping production GenAI, LLM, or AI-powered systems.
Advanced Python programming skills and experience building maintainable production software.
Hands-on experience with PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, or similar ML frameworks.
Experience with LLM applications, RAG systems, embeddings, vector databases, prompt engineering, and model evaluation.
Experience deploying AI / ML services using Docker, Kubernetes, CI/CD workflows, APIs, and cloud-native infrastructure.
Strong understanding of classical machine learning, deep learning, NLP, information retrieval, and model validation.
Ability to communicate complex AI concepts clearly to technical and non-technical stakeholders.
Experience mentoring engineers, reviewing technical designs, or leading complex AI engineering initiatives.

Senior AI Engineer

Key skills