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

• 8+ years in AI/ML engineering, data science, or related fields
• Advanced hands-on Python programming
• SQL proficiency with large-scale structured/unstructured datasets
• Strong ML and AI fundamentals
• Hands-on experience building and deploying LLM-based applications
• Expertise in agentic AI: orchestration, tool/function calling, planning, memory, context management
• Extensive hands-on Databricks AI Agents and Databricks ecosystem experience
• MLOps/LLMOps experience including CI/CD, model lifecycle, experiment tracking, production deployment
• Deploying/operating AI/ML or GenAI models in Azure, AWS, or GCP
• RAG architectures, embeddings, vector databases/search, prompt engineering, LLM evaluation
• LLM/GenAI frameworks (LangChain, LangGraph, Semantic Kernel, or similar)
• Enterprise-grade agentic AI platforms or multi-agent systems
• Databricks Model Serving, Vector Search, MLflow, Unity Catalog
• Kubernetes, Docker, REST APIs, microservices, CI/CD pipelines
• Managed GenAI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI)
• Vector databases (Pinecone, Azure AI Search, Weaviate, Databricks Vector Search)
• LLM optimization for latency, throughput, scalability, token consumption, cost
• Bachelor's in CS, Data Science, AI, IT, or related discipline
• ML/AI/data science certification (e.g., TensorFlow Developer, Databricks Certified)
• Cloud or MLOps certification (e.g., AWS ML Specialist, Azure AI Engineer Associate)