Data Science Lead
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 outcomesLead 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 scalabilityBuild, orchestrate, and optimize AI agents and autonomous workflows, focusing on robust memory, context management, and multi-agent architecturesDrive enterprise AI/ML workloads within the Databricks ecosystem, leveraging Databricks AI Agents, Model Serving, Vector Search, MLflow, and Unity Catalog to enhance operational efficiencyEstablish and maintain MLOps and LLMOps practices, including CI/CD pipelines, model lifecycle management, experiment tracking, evaluation, and monitoring for continuous improvementDevelop and apply RAG architectures, embeddings, vector databases, prompt engineering, and LLM evaluation frameworks to improve model performance and reliabilityEnsure AI security, responsible AI practices, data privacy, and effective mitigation of hallucination, prompt injection, and GenAI guardrailsMentor engineers and provide technical leadership, collaborating with cross-functional teams to deliver scalable, enterprise-grade AI solutionsRequired Skills:
Advanced hands-on programming experience in PythonProficiency in SQL and experience with large-scale structured and unstructured datasetsStrong practical understanding of machine learning and AI fundamentalsHands-on experience building and deploying LLM-based applicationsExpertise in agentic AI including agent orchestration, autonomous workflows, tool/function calling, planning, task decomposition, memory, and context managementExtensive hands-on experience with Databricks AI Agents and the Databricks ecosystemExperience with MLOps and LLMOps, including CI/CD, model lifecycle management, experiment tracking, and production deploymentProven experience deploying and operating AI/ML or GenAI models/applications in Azure, AWS, or GCPExpertise in RAG architectures, embeddings, vector databases/vector search, prompt engineering, and LLM evaluationProficiency with LLM and GenAI frameworks/orchestration tools such as LangChain, LangGraph, Semantic Kernel, or similar technologiesPreferred Skills:
Experience building enterprise-grade agentic AI platforms or multi-agent systemsExpertise with Databricks Model Serving, Vector Search, MLflow, Unity Catalog, and related Databricks AI/ML capabilitiesExperience with Kubernetes, Docker, REST APIs, microservices, and CI/CD pipelinesExperience with managed GenAI platforms such as Azure OpenAI, AWS Bedrock, or Google Vertex AIExperience with vector databases like Pinecone, Azure AI Search, Weaviate, or Databricks Vector SearchExperience optimizing LLM applications for latency, throughput, scalability, token consumption, and costDesired Qualifications:
Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Information Technology, or a closely related disciplineCertification in machine learning, AI engineering, or data science from a recognized institution such as TensorFlow Developer Certificate or Databricks Certified Professional Data ScientistCertification in cloud platforms or MLOps, for example AWS Certified Machine Learning Specialist or Azure AI Engineer Associate
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