Lead the design, build, and maintenance of scalable data pipelines and platforms, architect MDM and AI-driven workflows, and mentor engineering teams in a remote, global environment.
Responsibilities
Own and drive technical architecture for complex data initiatives
Design, build, and maintain scalable data pipelines and distributed platforms
Architect and lead enterprise Master Data Management
Integrate agentic AI and LLM-driven workflows
Build data foundations for machine learning and AI
Requirements
Bachelor's degree in Computer Science, Engineering, MIS, or related field
Own and drive the quality, reliability and evolution of AI systems in production, responsible for end-to-end delivery of AI features, production stability of AI systems, and data-driven experimentation.
Responsibilities
Take ownership of major AI features and LLM workflows and deliver them to production within agreed timelines
Own AI output quality, structure and predictability across user-facing AI interactions
Integrate and orchestrate multiple LLM providers through an AI gateway, managing model selection, fallback strategies and cost optimisation
Design and orchestrate tool-using and agentic AI workflows: clean tool contracts (including MCP-based tools), function-calling interfaces and reliable AI-to-system integrations
Build and maintain multi-step LLM workflows and RAG pipelines (indexing, embeddings, retrieval, re-ranking) for reasoning, context reuse and search
Serve as a hands-on individual contributor building and supporting AI/ML capabilities for the Behavioral Health portfolio.
Responsibilities
Build and support AI/ML capabilities for the Behavioral Health portfolio
Develop workflows, APIs, data pipelines, automated testing, and production-ready components
Focus on coding, model integration, prompt development, automated testing, data processing, deployment, and production support
Contribute reusable components across BH UM Modernization, BH Provider Digital Agentic AI, ABA AI, ABA Oversight and Supervision, and Provider Health Dashboard
Develop, test, and maintain AI/ML services, large language model workflows, APIs, data pipelines, and application components
Lead the design and development of scalable, enterprise-grade data platforms, with a strong specialization in Databricks and modern cloud technologies, driving complex, cross-functional data initiatives.
Responsibilities
Own and drive the technical architecture of complex, cross-functional data initiatives, covering ingestion, transformation, storage, and serving layers.
Design, develop, and maintain scalable, high-performance data pipelines and distributed data platforms using Databricks and cloud-native technologies across Azure, AWS, or GCP.
Lead enterprise Master Data Management (MDM) initiatives, including golden records, entity resolution, match-and-merge processes, data domains, reference data, hierarchies, and data stewardship.
Architect and integrate agentic AI and LLM-powered solutions, including autonomous agents, RAG pipelines, AI copilots, and orchestration frameworks, into enterprise data platforms.
Build and maintain data foundations for machine learning and AI applications, including feature stores, vector databases, embeddings, and ML/LLMOps pipelines.
Requirements
Bachelor's degree in Computer Science, Engineering, Management Information Systems, or a related discipline
Lead the design and development of scalable, enterprise-grade data platforms, with a strong specialization in Databricks and modern cloud technologies, driving complex, cross-functional data initiatives.
Responsibilities
Own and drive the technical architecture of complex, cross-functional data initiatives, covering ingestion, transformation, storage, and serving layers.
Design, develop, and maintain scalable, high-performance data pipelines and distributed data platforms using Databricks and cloud-native technologies across Azure, AWS, or GCP.
Lead enterprise Master Data Management (MDM) initiatives, including golden records, entity resolution, match-and-merge processes, data domains, reference data, hierarchies, and data stewardship.
Architect and integrate agentic AI and LLM-powered solutions, including autonomous agents, RAG pipelines, AI copilots, and orchestration frameworks, into enterprise data platforms.
Build and maintain data foundations for machine learning and AI applications, including feature stores, vector databases, embeddings, and ML/LLMOps pipelines.
Requirements
Bachelor's degree in Computer Science, Engineering, Management Information Systems, or a related discipline
Lead the design and development of scalable, enterprise-grade data platforms, with a strong specialization in Databricks and modern cloud technologies, driving complex, cross-functional data initiatives.
Responsibilities
Own and drive the technical architecture of complex, cross-functional data initiatives, covering ingestion, transformation, storage, and serving layers.
Design, develop, and maintain scalable, high-performance data pipelines and distributed data platforms using Databricks and cloud-native technologies across Azure, AWS, or GCP.
Lead enterprise Master Data Management (MDM) initiatives, including golden records, entity resolution, match-and-merge processes, data domains, reference data, hierarchies, and data stewardship.
Architect and integrate agentic AI and LLM-powered solutions, including autonomous agents, RAG pipelines, AI copilots, and orchestration frameworks, into enterprise data platforms.
Build and maintain data foundations for machine learning and AI applications, including feature stores, vector databases, embeddings, and ML/LLMOps pipelines.
Requirements
Bachelor's degree in Computer Science, Engineering, Management Information Systems, or a related discipline
Lead the design and development of scalable, enterprise-grade data platforms, with a strong specialization in Databricks and modern cloud technologies, driving complex, cross-functional data initiatives.
Responsibilities
Own and drive the technical architecture of complex, cross-functional data initiatives, covering ingestion, transformation, storage, and serving layers.
Design, develop, and maintain scalable, high-performance data pipelines and distributed data platforms using Databricks and cloud-native technologies across Azure, AWS, or GCP.
Lead enterprise Master Data Management (MDM) initiatives, including golden records, entity resolution, match-and-merge processes, data domains, reference data, hierarchies, and data stewardship.
Architect and integrate agentic AI and LLM-powered solutions, including autonomous agents, RAG pipelines, AI copilots, and orchestration frameworks, into enterprise data platforms.
Build and maintain data foundations for machine learning and AI applications, including feature stores, vector databases, embeddings, and ML/LLMOps pipelines.
Requirements
Bachelor's degree in Computer Science, Engineering, Management Information Systems, or a related discipline
Lead the design and development of scalable, enterprise-grade data platforms, with a strong specialization in Databricks and modern cloud technologies, driving complex, cross-functional data initiatives.
Responsibilities
Own and drive the technical architecture of complex, cross-functional data initiatives, covering ingestion, transformation, storage, and serving layers.
Design, develop, and maintain scalable, high-performance data pipelines and distributed data platforms using Databricks and cloud-native technologies across Azure, AWS, or GCP.
Lead enterprise Master Data Management (MDM) initiatives, including golden records, entity resolution, match-and-merge processes, data domains, reference data, hierarchies, and data stewardship.
Architect and integrate agentic AI and LLM-powered solutions, including autonomous agents, RAG pipelines, AI copilots, and orchestration frameworks, into enterprise data platforms.
Build and maintain data foundations for machine learning and AI applications, including feature stores, vector databases, embeddings, and ML/LLMOps pipelines.
Requirements
Bachelor's degree in Computer Science, Engineering, Management Information Systems, or a related discipline
Lead the design and development of scalable, enterprise-grade data platforms, with a strong specialization in Databricks and modern cloud technologies, driving complex, cross-functional data initiatives.
Responsibilities
Own and drive the technical architecture of complex, cross-functional data initiatives, covering ingestion, transformation, storage, and serving layers.
Design, develop, and maintain scalable, high-performance data pipelines and distributed data platforms using Databricks and cloud-native technologies across Azure, AWS, or GCP.
Lead enterprise Master Data Management (MDM) initiatives, including golden records, entity resolution, match-and-merge processes, data domains, reference data, hierarchies, and data stewardship.
Architect and integrate agentic AI and LLM-powered solutions, including autonomous agents, RAG pipelines, AI copilots, and orchestration frameworks, into enterprise data platforms.
Build and maintain data foundations for machine learning and AI applications, including feature stores, vector databases, embeddings, and ML/LLMOps pipelines.
Requirements
Bachelor's degree in Computer Science, Engineering, Management Information Systems, or a related discipline
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