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