AI Software Developer

AI Software Developer About the RoleWe are looking for a sharp, product-minded AI Software Developer who can own end-to-end feature delivery — from understanding a business requirement to shipping production-grade AI-powered capabilities. You will build real product features that leverage the latest in AI and integrate them into a robust backend architecture in HealthCare field. The ideal candidate is someone who can pick up any technology fast, writes clean backend code, is fluent in SQL, and stays ahead of the curve on AI tooling, frameworks and latest products.What You’ll Do• Design, develop, and deploy AI-powered product features from concept to production, working closely with product and engineering teams.• Build and maintain performant backend services in Python, integrating AI/ML models and APIs into the product stack.• Evaluate, adopt, and integrate emerging AI tools, frameworks, and models — including LLM orchestration (LangChain, LlamaIndex, etc.), vector databases, RAG pipelines, and prompt engineering best practices.• Architect scalable, secure, and maintainable systems that handle production-level traffic and data volumes.• Collaborate cross-functionally with product managers, designers, and domain experts to translate business requirements into technical solutions.• Participate in code reviews, technical design discussions, and continuous improvement of development practices.• Stay current with the rapidly evolving AI/ML landscape and proactively bring new ideas and approaches to the team.Required QualificationsCore Software Engineering• 4+ years of professional software development experience, or equivalent demonstrable skill through projects, open-source contributions, or portfolio work.• Strong backend engineering skills with production experience in Python.• Deep proficiency in SQL — comfortable writing complex queries, optimizing performance, designing schemas, and working with PostgreSQL and/or MySQL.• Experience designing and building RESTful APIs and/or GraphQL services.• Solid understanding of software engineering fundamentals: data structures, algorithms, design patterns, version control (Git), and CI/CD pipelines.• Experience working with GCP services (Cloud Run, Cloud SQL, Pub/Sub, etc.) or equivalent cloud platforms.AI & Machine Learning• Hands-on experience using modern AI tools and frameworks in a product context — not just prototyping, but shipping features to users.• Working knowledge of LLM integration: API usage, prompt engineering, fine-tuning, and evaluation.• Familiarity with AI orchestration frameworks such as LangChain, LlamaIndex, CrewAI, or similar.• Understanding of vector databases and retrieval-augmented generation (RAG) patterns.• Ability to evaluate and compare AI models, tools, and approaches and make pragmatic decisions based on product needs, cost, and performance.Mindset & Approach• Smart, resourceful problem-solver who can figure out any technology based on the product requirement — you don’t wait to be taught.• Product-oriented thinker: you care about why a feature is being built, not just how.• Strong communicator who can articulate technical decisions to both technical and non-technical stakeholders.• Self-motivated with the ability to work independently while collaborating effectively with a team.Preferred QualificationsHealthcare Domain Experience• Prior experience building software in healthcare or health-tech environments.• Working knowledge of HIPAA compliance requirements and handling Protected Health Information (PHI) in software systems.• Familiarity with healthcare data standards such as HL7 and FHIR.• Understanding of Business Associate Agreements (BAAs) and their implications for AI systems processing patient data.• Experience with de-identification of health data for model training and analytics.Agentic Systems Experience• Experience designing and building autonomous or semi-autonomous AI agent systems that perform multi-step tasks with minimal human intervention.• Familiarity with agentic frameworks and patterns: tool use, function calling, memory and context management, planning, and self-correction loops.• Exposure to multi-agent orchestration, task decomposition, and agent-to-agent communication patterns.• Understanding of safety, guardrails, and human-in-the-loop considerations when deploying agentic AI in production. Tech Stack at a GlanceCategoryTechnologiesBackend LanguagePythonDatabasesPostgreSQL, MySQLCloud PlatformGoogle Cloud Platform (GCP)AI/ML FrameworksLangChain, LlamaIndex, CrewAI, and similarLLM ProvidersOpenAI, Anthropic, Google Gemini, open-source modelsVector DatabasesPinecone, Weaviate, ChromaDB, pgvectorDevOps & ToolsGit, CI/CD, Docker, GCP services