Data Science Lead - R01570347

Data Science Lead

Job requirements Experience Range: With at least 8 years of experience in AI/ML engineering, machine learning, data science, software engineering, or related fields Key Responsibilities: - Design and implement advanced machine learning and AI solutions, including LLM-based applications and agentic AI systems, to address complex business challenges and deliver measurable business outcomes - Lead the development, deployment, and operation of production AI/ML and GenAI models across major cloud platforms such as Azure, AWS, or GCP, ensuring high availability and scalability - Build, orchestrate, and optimize AI agents and autonomous workflows, focusing on robust memory, context management, and multi-agent architectures - Drive enterprise AI/ML workloads within the Databricks ecosystem, leveraging Databricks AI Agents, Model Serving, Vector Search, MLflow, and Unity Catalog to enhance operational efficiency - Establish and maintain MLOps and LLMOps practices, including CI/CD pipelines, model lifecycle management, experiment tracking, evaluation, and monitoring for continuous improvement - Develop and apply RAG architectures, embeddings, vector databases, prompt engineering, and LLM evaluation frameworks to improve model performance and reliability - Ensure AI security, responsible AI practices, data privacy, and effective mitigation of hallucination, prompt injection, and GenAI guardrails - Mentor engineers and provide technical leadership, collaborating with cross-functional teams to deliver scalable, enterprise-grade AI solutions Required Skills: - Advanced hands-on programming experience in Python - Proficiency in SQL and experience with large-scale structured and unstructured datasets - Strong practical understanding of machine learning and AI fundamentals - Hands-on experience building and deploying LLM-based applications - Expertise in agentic AI including agent orchestration, autonomous workflows, tool/function calling, planning, task decomposition, memory, and context management - Extensive hands-on experience with Databricks AI Agents and the Databricks ecosystem - Experience with MLOps and LLMOps, including CI/CD, model lifecycle management, experiment tracking, and production deployment - Proven experience deploying and operating AI/ML or GenAI models/applications in Azure, AWS, or GCP - Expertise in RAG architectures, embeddings, vector databases/vector search, prompt engineering, and LLM evaluation - Proficiency with LLM and GenAI frameworks/orchestration tools such as LangChain, LangGraph, Semantic Kernel, or similar technologies Preferred Skills: - Experience building enterprise-grade agentic AI platforms or multi-agent systems - Expertise with Databricks Model Serving, Vector Search, MLflow, Unity Catalog, and related Databricks AI/ML capabilities - Experience with Kubernetes, Docker, REST APIs, microservices, and CI/CD pipelines - Experience with managed GenAI platforms such as Azure OpenAI, AWS Bedrock, or Google Vertex AI - Experience with vector databases like Pinecone, Azure AI Search, Weaviate, or Databricks Vector Search - Experience optimizing LLM applications for latency, throughput, scalability, token consumption, and cost Desired Qualifications: - Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Information Technology, or a closely related discipline - Certification in machine learning, AI engineering, or data science from a recognized institution such as TensorFlow Developer Certificate or Databricks Certified Professional Data Scientist - Certification in cloud platforms or MLOps, for example AWS Certified Machine Learning Specialist or Azure AI Engineer Associate