About the role

Data Science LeadJob requirementsExperience 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

Data Science Lead - R01570347

About the role

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