Source description
About the role
Technical Leadership & Architecture
Lead architecture, design, development, testing, and deployment of enterprise software solutions (applications, data, integration, AI agents, AI models)
Participate in strategy and roadmap discussions, architecture definition, and technology evaluations
Mentor engineers through design reviews, code reviews, and technical guidance
Quickly evaluate and adopt new tools, technologies, and platforms to build prototypes and proofs-of-concept
Drive adoption of AI solutions and collaborate with engineering teams, product leaders, and domain experts to deliver results
Software Engineering
Design, develop, and maintain scalable, production-grade enterprise applications using modern languages and frameworks (Python, Java, C#, JavaScript)
Define and enforce coding standards, best practices, and design patterns across the team
Build and maintain CI/CD pipelines, infrastructure-as-code, and cloud-deployed services (AWS, Azure)
Integrate enterprise systems via APIs, event-driven architectures, and messaging platforms
Identify and resolve performance bottlenecks, technical debt, and system reliability issues
Data & Analytics
Work with large, complex datasets and ensure data quality and integrity
Analyze data to generate insights that inform both model development and broader solution strategy
Collaborate with stakeholders to translate business problems into data-driven solutions
AI / Machine Learning Development
Design, develop, and train machine learning and deep learning models for healthcare and insurance use cases
Perform data modeling and feature engineering to support model development
Develop custom model metrics and approaches tailored to specific business problems
Ensure models are scalable, reliable, and integrated into production systems
Agentic AI Development
Design and develop LLM-powered workflows and agentic systems that help users complete complex tasks, retrieve information, reason over enterprise data, or interact with internal systems.
Integrate LLMs with tools, APIs, databases, documents, and enterprise platforms using patterns such as function calling, MCP, RAG, and structured tool use.
Architect orchestration patterns for planning, task decomposition, memory, context management, and human-in-the-loop review where appropriate.
Develop orchestration layers to manage agent planning, memory, task decomposition, and execution loops
Evaluate agentic systems for correctness, reliability, safety, observability, auditability, and harden them for production readiness.
Stay current with emerging AI frameworks and platforms, selecting tools pragmatically based on client needs.
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