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