Full Stack Engineer

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Senior Full Stack Engineer

Contract Position

Position Overview

We are seeking an experienced Senior Full Stack & Cloud-Native AI Engineer to transform an existing Data Insight proof of concept into a secure, scalable, production-ready enterprise application.

The application enables users to explore reporting data, ask contextual follow-up questions, and gain actionable insights supported by verifiable source evidence.

In this role, you will design and develop end-to-end application capabilities, including agentic AI workflows, full-stack user experiences, cloud-native services, and enterprise integrations. You will play a critical role in establishing the evaluation, testing, and monitoring frameworks required to ensure the accuracy, reliability, security, and continuous improvement of AI-powered functionality.

The ideal candidate combines strong full-stack engineering expertise with hands-on experience developing production-grade AI applications, particularly agentic workflows, retrieval-augmented generation (RAG), and AI evaluation frameworks.

Key Responsibilities

Agentic AI Application Development

• Design, develop, and deploy production-grade agentic AI applications using large language models (LLMs), tool calling, structured outputs, and multi-step reasoning workflows.
• Build intelligent workflows that interpret user intent, maintain conversation context, orchestrate tools and APIs, and support human review when appropriate.
• Implement prompt engineering, context management, workflow orchestration, and error-handling strategies to improve application reliability and response quality.
• Integrate retrieval-augmented generation (RAG) capabilities to ground AI-generated responses in trusted enterprise data and source documents.

Full Stack & Cloud-Native Development

• Develop responsive, user-friendly web applications using Angular and modern front-end development practices.
• Design and implement scalable backend services and RESTful APIs using C# and .NET.
• Develop Python-based AI services and agent orchestration components.
• Integrate application services with existing enterprise reporting platforms, databases, authentication providers, and data-access systems.
• Build and deploy cloud-native solutions using Microsoft Azure and associated cloud services.
• Implement secure API integrations, asynchronous processing, and resilient service-to-service communication.

AI Evaluation, Testing & Quality Engineering

• Own the design and implementation of comprehensive evaluation frameworks for AI-powered applications.
• Collaborate with healthcare domain experts and product owners to curate representative evaluation datasets and define scoring rubrics, quality metrics, and acceptance criteria.
• Develop automated evaluation processes to assess:
• Accuracy and relevance of AI-generated answers.
• Correctness and traceability of supporting evidence.
• Tool selection, execution, and workflow orchestration.
• Context retention and multi-turn conversation quality.
• End-to-end task completion and user experience.
• Handling of ambiguous, unsupported, and out-of-scope requests.
• Combine automated evaluation with human review to validate analytical accuracy and domain-specific correctness.
• Establish regression testing and baseline comparisons to identify quality degradation resulting from changes to models, prompts, datasets, or application code.
• Integrate AI evaluation suites, automated tests, and quality gates into CI/CD pipelines.
• Maintain version-controlled prompts, evaluation datasets, test configurations, and performance baselines.

Reliability, Security & Production Operations

• Design resilient execution workflows incorporating timeouts, retries, cancellation, durable state management, and recovery mechanisms.
• Implement authentication, authorization, and data-access controls across APIs, AI agents, tools, and underlying data sources.
• Ensure sensitive healthcare and enterprise data is handled securely and in accordance with applicable organizational policies.
• Establish observability through application logging, distributed tracing, performance monitoring, and error reporting.
• Monitor production AI quality, response latency, operational costs, and failure rates.
• Analyze production feedback and failures, convert findings into reproducible test cases, and drive measurable improvements.
• Optimize application performance, scalability, reliability, and cloud operating costs.

Data Engineering & Analytical Integrity

• Develop and optimize SQL queries, relational data models, and data-access services.
• Ensure analytical results are accurate, reproducible, and supported by traceable source data.
• Address data-quality challenges, including missing values, inconsistencies, numerical precision, and aggregation accuracy.
• Work closely with business stakeholders and healthcare domain experts to validate analytical findings and communicate uncertainty and system limitations.

Required Qualifications

• 8+ years of professional software engineering experience delivering and supporting secure, scalable enterprise applications.
• Hands-on experience designing and developing agentic AI applications using LLMs, tool calling, structured outputs, prompt engineering, and multi-step workflows.
• Strong full-stack development experience with Angular, C#/.NET Web APIs, and Python.
• Practical experience building AI evaluation and quality pipelines, including evaluation datasets, scoring methodologies, automated regression testing, and human-in-the-loop validation.
• Demonstrated ability to use evaluation results and production feedback to improve AI application quality and reliability.
• Strong experience with SQL, relational database design, data modeling, and analytical data processing.
• Experience building and deploying cloud-native applications using Microsoft Azure.
• Hands-on experience with CI/CD pipelines, automated unit testing, integration testing, and end-to-end testing.
• Strong understanding of enterprise application security, authentication, authorization, secure API design, and data-access controls.
• Experience implementing observability, distributed tracing, asynchronous processing, and fault-tolerant application architectures.
• Strong analytical, problem-solving, communication, and collaboration skills.
• Ability to work independently and effectively collaborate with engineering teams, product owners, and business/domain experts.

Preferred Qualifications

• Experience implementing retrieval-augmented generation (RAG), including document ingestion, embedding generation, vector search, retrieval strategies, and grounding evaluation.
• Experience with healthcare analytics, enterprise reporting platforms, or data-intensive applications involving sensitive information.
• Experience transitioning AI proof-of-concept solutions into production-grade enterprise applications.
• Familiarity with AI quality metrics, LLM-as-a-judge approaches, semantic evaluation, groundedness assessment, and human evaluation workflows.
• Experience optimizing AI application performance, model selection, inference latency, token consumption, and cloud operating costs.
• Familiarity with Azure AI services and enterprise AI application architecture.