Brillio

Data Analyst

• Lead Data Analyst needed in New York, NY and various unanticipated locations throughout the U.S.
• Utilize the power of solutions that can be driven through Visualization and ETL tools.
• Establish connection with different teams to evaluate the automation and service management offerings Python can bring in.
• Drive discussions with various teams to foresee opportunities to unify the operations console across businesses.
• Implementation of Customer Dashboard and Reports for real-time performance view of SLA.
• Work on SQL Server (or client specific RDBMS) developing customized codes and testing.
• Responsible for creating operational insights for telecom network related data from Fuze and integrate with other data sources to identify revenue growth/cost optimization/operational efficiency opportunities.
• Interpret data and define new KPIs for data analysis from multiple sources.
• Use data visualization and other data-gathering methods to either provide internal strategic direction or guide decisions for other teams for new tower builds, OFS analysis, Fallout analysis, compliance and usage adoption.
• Work on acquiring data from primary or secondary data sources and preserve the data integrity of these sources using SQL, Python and other analytical tools.
• Create powerful data visualizations using tools like Tableau and Looker.
• Focus on the overall service quality and timely execution of the project.
• Active participation and contribution to all service improvement initiatives to ensure they are aligned to our customers’ expectations.
• Successfully implement multiple Analytics/IT projects by providing the technology knowledge and vision to support the company’s business objectives and evaluate and recommend all software for business processes.
• Be accountable for all aspects of service delivery in conceptualization, planning, design, automation, transition and delivery.
• Understand clearly operational definitions for all processes; understand how to measure all critical data points in the processes, improve any irregularities reported because of the Metrics Dashboards.
• Proactively initiate Process Improvements keeping in mind inputs from Audit Process. Manage Process & Framework.

Senior Lead Data Analyst

Product Manager

• Define product strategy and roadmap for software products supporting pharma manufacturing and operations.
• Lead end-to-end product development from concept through launch and continuous improvement.
• Work closely with engineering, design, QA, operations, and business stakeholders to translate business needs into clear product requirements.
• Gather, prioritize, author, and manage product requirements, user stories, and acceptance criteria.
• Collaborate with geographically distributed cross-functional teams across time zones.
• Ensure solutions align with pharma manufacturing workflows, compliance expectations, and enterprise system integrations (e.g. ERP, SCADA, LIMS).
• Partner with stakeholders to support product adoption, training, and release readiness.
• Track product performance, gather feedback, and use data to guide roadmap decisions.

Pharma Product Manager

• Identify & Prioritize High-Impact AI Use Cases
• Drive Rapid Experimentation
• Build & Deliver AI-Enabled Products
• Bridge Business, Clinical, and Technology Teams
• Embed AI into Real Workflows
• Measure Impact & Scale

AI Engineer

• Design, build, and productionize AI-augmented and agentic software systems, with a strong focus on Microsoft Copilot suite across development and productivity workflows.
• Use GitHub Copilot and modern IDE-based “vibe coding” approaches to accelerate software delivery while maintaining production-grade quality.
• Develop multi-step, tool-using AI agents that can reason, plan, execute, and validate outcomes across real-world systems.
• Apply agentic software development patterns (task decomposition, memory, orchestration, feedback loops, guardrails).
• Integrate and evaluate multiple LLM providers, including Claude, selecting the right model based on reasoning depth, cost, latency, and safety.
• Translate ambiguous business problems into well-structured AI-driven solutions with clear constraints and measurable outcomes.
• Own solutions end-to-end: architecture, implementation, testing, deployment, monitoring, and iteration.
• Produce concise technical documentation and explain design decisions clearly to architects, product owners, and executives.

Lead AI Engineer – Copilot, Agentic Development

• Reimagine and redesign healthcare processes using AI-first principles
• Identify opportunities to replace or augment manual workflows with AI agents
• Partner with business stakeholders to translate problems into working AI solutions
• Design, build, and deploy AI agents for enterprise workflows
• Implement multi-agent systems using LLMs (prompting, memory, tool usage)
• Rapidly prototype and productionize solutions using platforms such as: OpenAI, Anthropic, Google (Gemini)
• Embed with client teams (business + IT) to Whiteboard solutions
• Build prototypes in days/weeks
• Deploy and iterate rapidly
• Operate as a trusted advisor + builder, not just a developer
• Leverage AI across the full software lifecycle: Code generation and refactoring, Automated testing and validation, Documentation and observability
• Drive productivity improvements across engineering teams
• Train business and ops teams to use AI tools and agents effectively, build and iterate workflows independently, develop reusable playbooks and frameworks
• You will work on transforming core healthcare processes such as: Claims Processing & Adjudication, Automate intake, validation, and exception handling
• Prior Authorization - AI-driven clinical review, decision support, and workflow orchestration
• Member & Provider Contact Center - Intelligent agents for call routing, resolution, and digital containment
• Provider Data Management - Data intake, validation, and directory accuracy
• Care Management & Utilization Review - AI-assisted case management and risk identification
• Revenue Cycle & Payment Integrity - Fraud, waste, and abuse detection using AI

Forward Deployed AI Engineer

Sales

• Own net-new logo acquisition across Healthcare Payer organizations
• Build and execute territory and target account plans
• Prospect and engage CXOs (CIO, CDO, CTO, COO, Digital, Operations leaders)
• Create demand around:
• Digital transformation programs
• Data, analytics, and platform modernization
• Digital front door and customer experience initiatives
• Call center, operations, and workflow transformation
• Cloud migration, application modernization, and product engineering
• Drive opportunities from cold start → qualification → deal close
• Shape deals, articulate value, and lead commercial negotiations
• Partner closely with industry leadership, pre-sales, and delivery teams
• Maintain strong pipeline hygiene and forecast accuracy

Sales Director – HLS/Payor

Backend Engineer

• Design, build, and maintain backend services, APIs, and microservices using Golang, Java and Python
• Develop RESTful and/or event‑driven APIs with a focus on performance, scalability, and reliability
• Write clean, maintainable, and well‑tested code following best engineering practices
• Collaborate with cross‑functional teams to translate business requirements into technical solutions
• Optimize application performance, troubleshoot production issues, and implement fixes
• Implement secure coding practices, authentication, authorization, and data protection standards
• Participate in code reviews, design discussions, and architecture decisions
• Support CI/CD pipelines and work closely with DevOps teams for deployments and monitoring
• Contribute to documentation, technical standards, and continuous improvement initiatives

Senior Golang Developer, Java/Python

Software Engineer

• Develop and maintain React-based user interfaces.
• Build reusable and scalable React components.
• Implement and enhance drag-and-drop functionality within the application.
• Ensure responsive and high-performance web applications.
• Collaborate with designers, backend developers, and product teams to deliver features.
• Debug, troubleshoot, and resolve frontend issues.
• Write clean, maintainable, and well-documented code.
• Participate in code reviews and follow best practices.

Software Development Lead

Data Scientist

• Develop and optimize speech-to-text models using deep learning techniques.
• Design and implement post-processing pipelines using LLMs to enhance transcription accuracy, formatting, and contextual understanding.
• Build and deploy end-to-end machine learning pipelines for speech and text processing.
• Fine-tune and evaluate large language models for domain-specific use cases.
• Work with large-scale audio and text datasets, ensuring quality and consistency.
• Collaborate with data engineers and software teams to integrate models into production systems.
• Optimize models for performance, scalability, and latency.
• Conduct experiments, analyze results, and continuously improve model performance.
• Ensure data governance, privacy, and ethical AI practices.
• Document models, methodologies, and findings for knowledge sharing.

Senior Data Scientist

Frontend Engineer

• Design, develop, and maintain modern web applications using React and TypeScript
• Implement responsive and accessible user interfaces based on UX/UI designs and business requirements
• Build reusable frontend components
• Consume and integrate RESTful APIs and backend services
• Optimize application performance, cross‑browser compatibility, and responsiveness
• Write unit and integration tests to ensure code quality and reliability
• Collaborate in Agile teams through sprint planning, daily stand‑ups, and retrospectives
• Participate in code reviews, design discussions, and architectural decisions
• Troubleshoot frontend production issues
• Contribute to documentation, standards, and continuous improvement initiatives

Frontend Developer – React, Typescript

Golang Backend Developer – Java, Python

Analytics Engineer

• Architect the Business Insights Foundation
• Partner with data engineers to design, build, and maintain analytics transformation layers that act as a single source of truth for downstream reporting, BI, and data science use cases.
• Deliver Business Facing Insights
• Own the complete analytics lifecycle—from requirements gathering and data modeling to scalable dashboards and self service analytics solutions.
• Stakeholder Collaboration
• Serve as the technical analytics partner to business stakeholders, translating functional and strategic needs into automated, scalable data solutions.
• Data Storytelling
• Develop compelling dashboards, metrics, and analytical narratives that communicate insights effectively to both technical and non technical audiences.
• Build for Scale & Reusability
• Design standardized, reusable business metrics and data models that support automated reporting and enable deep dive self service analysis.
• Apply Engineering Best Practices
• Improve analytics infrastructure using software engineering principles such as version control, modular design, testing, documentation, and performance optimization.

Senior Analytics Engineer

Full-stack Engineer

• Lead Engineer for Digital Engineering team at Brillio

Lead Engineer

Analyst

• System Analyst to support the Payments area
• Work with clients to define digital strategy and execution roadmap
• Design and implement differentiated digital solutions
• Data Analysis
• Identifying Requirements
• Advanced reporting based on data analysis

System Analyst, Banking and Payments

Technical Product Manager

• Lead a team of engineers, researchers, and data scientists, performing QA on AI agent outputs
• Evaluate RAG pipeline architectures, challenge prompt engineering decisions, and review system designs
• Build relationships with internal business teams, understanding workflows and translating pain points into actionable technical work
• Set priorities and decide what the team works on across both AI Enablement and AI Experiments tracks
• Serve as the primary liaison with internal business teams, gather requirements, and validate priorities
• Communicate progress clearly to leadership and identify new internal processes where AI could reduce effort
• Guide the team’s technology roadmap, evaluate architecture decisions and optimize AI infrastructure spend