Описание
The client is building a healthcare platform focused on scaling its core product capabilities through AI and cloud-native technologies. The initiative combines product engineering, machine learning, and platform architecture, with a dedicated engineering team responsible for platform architecture and accelerating product development.
Задачи
•Lead the ideation, design, and execution of AI proofs of concept and end-to-end machine learning systems, from research and experimentation through scalable production deployment
•Develop and implement machine learning and AI systems across Computer Vision, NLP, and Generative AI to solve business and product challenges
•Collaborate with engineering, product management, and business stakeholders to translate business objectives into scalable AI capabilities and production-ready solutions
•Architect and maintain production-grade AI systems throughout their lifecycle, including data ingestion, feature engineering, model training, retrieval pipelines, orchestration, deployment, evaluation, and monitoring
•Design and implement RAG pipelines, semantic search systems, and multi-agent workflows, selecting architectures based on performance, latency, cost, and compliance constraints
•Design evaluation frameworks for machine learning and generative AI systems, including offline evaluation, human-in-the-loop validation, automated scoring, and production monitoring
•Optimize AI systems for scalability, latency, and cost efficiency, including batching, caching, quantization, and model selection
•Design and apply mathematical models and statistical methods to analyze large multimodal datasets, including time series, classification, regression, and representation learning
•Collaborate on AI architecture decisions and contribute to technical roadmap planning, identifying opportunities for AI adoption across the organization
•Mentor and provide technical guidance to junior ML engineers and data scientists, promoting engineering best practices and continuous learning
•Stay current with AI and machine learning research, tools, and architectures, evaluating and introducing relevant advancements
Требования
•5+ Years of hands-on experience in machine learning, AI research, or data science, with a track record of delivering production-grade ML solutions
•Master's or Ph.D. in Computer Science, Data Science, Machine Learning, or a related quantitative field
•Strong expertise in supervised and unsupervised learning, time series modeling, classification, regression, and model evaluation
•Hands-on experience developing production systems in Computer Vision, NLP, and Generative AI
•Experience building LLM-based applications, including RAG pipelines, agentic workflows, semantic search, and tool-using AI systems
•Experience designing generative AI evaluation frameworks, including prompt evaluation, hallucination detection, and performance monitoring
•Proficiency in Python and the ML ecosystem, including PyTorch, TensorFlow, Hugging Face, NumPy, Pandas, and scikit-learn
•Experience with LLM orchestration and serving frameworks such as LangChain, LlamaIndex, DSPy, Haystack, or equivalent
•Experience deploying and scaling AI systems on AWS, Azure, or GCP, including managed ML services and containerized deployments
•Experience building and maintaining end-to-end ML pipelines for data processing, model training, deployment, and monitoring
•Understanding of responsible AI principles, including bias mitigation, safety guardrails, and governance
•Proficiency with Git and software engineering best practices for collaborative ML development
•Excellent communication skills and ability to explain complex technical concepts to technical and non-technical stakeholders
Nice to have:
•MLOps tooling and workflows, including MLflow, Apache Airflow, Terraform, and CI/CD pipelines for model deployment
•Model serving and inference optimization tools such as vLLM
•Feature stores, real-time data pipelines, or streaming AI systems
•Distributed or large-scale model training frameworks
•Multimodal AI systems
•Fine-tuning LLMs or parameter-efficient training techniques such as LoRA and PEFT
•Contributions to open-source ML projects or peer-reviewed publications
Условия
No conditions specified
