Overview

• Backend Engineering: Develop robust APIs and backend services for claims processing, eligibility checks, and other healthcare operations using RESTful standards.

Responsibilities

• Backend Engineering: Develop robust APIs and backend services for claims processing, eligibility checks, and other healthcare operations using RESTful standards.
• Design and Develop Scalable Solutions: Architect and implement cloud-native applications using Python, microservices, and Kubernetes for healthcare data processing and analytics.
• Data Engineering: Design and maintain data pipelines for structured and unstructured healthcare data using PostgreSQL, MongoDB, and cloud-native tools.
• Frontend Development: Collaborate with UI/UX teams to build responsive and intuitive interfaces using Angular or React for healthcare applications.
• Healthcare Domain Expertise: Apply deep understanding of US healthcare systems, including claims, EDI transactions, and payer-provider workflows, to guide technical decisions.
• Cross-functional Collaboration: Work closely with product managers, data scientists, and QA teams to deliver high-quality software aligned with business goals.
• Participate in code reviews, documentation, and knowledge sharing to maintain high engineering standards.
• Security and Compliance: Ensure solutions adhere to HIPAA and other healthcare compliance standards, with secure data handling and access controls.
• Performance Optimization: Monitor and optimize system performance, scalability, and reliability in production environments.
• Mentorship and Leadership: Guide junior engineers, conduct code reviews, and promote best practices in software development and AI engineering.
• Preferred Tech Stack Contributions: Contribute to projects using Go and TypeScript where applicable, especially in performance-critical or frontend-heavy modules.
• Stay updated with emerging AI trends and contribute to internal innovation and proof-of-concept initiatives.
• B.E/B.Tech in Computer Sciences, IT, Engineering

Qualifications

• 6 to 7 years of Programming experience
• Advanced proficiency in Pythonwith expertise in building scalable microservices using RESTful (FastAPI, Snaic) with low latency.
• Expertise in containerization technologies (Docker, Kubernetes)
• Software Engineering & Development: Strong coding practices with Python, and experience in microservices, test-driven development, and concurrency.
• DevOps & Infrastructure: Experience with CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes).
• Cloud Optimization: Familiarity with deploying AI workloads on GCP or any other cloud platforms.
• Version control and experiment trackingusing Git, MLflow, and other MLOps tools for reproducibility and collaboration.
• Proficiency in scripting languages (Bash, PowerShell)
• Knowledge of agile methodologies
• Healthcare AI Applications: Understanding of healthcare-specific data modalities, privacy constraints, and domain adaptation for clinical and operational use cases.
• Evaluation Methodologies: Proficiency in designing benchmarks, conducting human evaluations, and applying automated metrics for model performance and safety.
• Mathematical Foundations: Strong grasp of linear algebra, probability, optimization theory, and information theory relevant to deep learning and model design.
• Research Methodology: Experience in experimental design, reproducibility, statistical analysis, and peer-reviewed publication processes.
• Strong problem-solving and analytical skills, with the ability to translate complex AI concepts into scalable engineering solutions.
• Ability to rapidly prototype and iterateon GenAI and LLM-based applications, balancing innovation with performance and reliability.
• Effective collaboration across cross-functional teams, including data scientists, researchers, and product stakeholders, to deliver impactful AI solutions.
• Clear and concise communication skills, capable of presenting technical ideas to both technical and non-technical audiences.
• Commitment to engineering excellence, including writing clean, maintainable code, conducting thorough code reviews, and following best practices in software development.
• Proactive learning mindset, staying current with emerging trends in AI, GenAI, and agentic systems, and applying them to real-world problems.
• Ownership and accountabilityin delivering high-quality solutions under tight deadlines and evolving requirements.
• Focus on reproducibility and reliability, using tools like Git, MLflow, and CI/CD pipelines to ensure consistent experimentation and deployment.
• Ethical and responsible AI development, with awareness of safety, fairness, and privacy considerations in model design and deployment