AI Full Stack Software Engineer

About the job

Senior Software Engineer (AI-Native)Location: Hyderabad, India

Employment: Full-time · Early-stage, founder-led company

Compensation: 6LPA to 24LPA range

About Us

We are an early-stage company building applied AI products for complex professional workflows, including healthcare and legal.Our focus is practical: software that professionals can trust and use in their daily work. That requires more than an impressive AI demo—it requires reliable engineering, thoughtful user experiences, strong data foundations, and disciplined validation.We operate with a small team, direct founder involvement, and AI-assisted development as standard practice. Specific product details will be shared during the interview process.

The RoleWe are looking for a hands-on engineering leader who can own multiple AI products end-to-end—not just an AI component, backend service, or prototype.You will take products from requirements and architecture through implementation, integration, testing, deployment, and ongoing operation. The work spans AI-powered applications, voice and document workflows, knowledge systems, and the shared infrastructure supporting them.You will work directly with the founder, who leads product direction, priorities, and customer requirements. You will own engineering execution and technical decisions, enabling the founder to focus on product rather than daily code reviews, debugging, and integration management.This is a player-coach role: you will write production code, lead a small engineering team, and remain accountable for whether the complete product actually works.

• What You Will OwnEnd-to-end product delivery. Turn product requirements into working applications across the frontend, backend, AI layer, data systems, and infrastructure. Own the complete user journey—not merely whether individual components run.
• Architecture and technical decisions. Choose appropriate technologies, define clear interfaces, and make practical build-versus-buy decisions. Build reusable capabilities across products without creating unnecessary abstraction or complexity.
• Applied AI engineering. Build and integrate LLM-powered features, retrieval and knowledge workflows, structured outputs, and speech or voice capabilities. Know when to use a model, conventional software, deterministic validation, or human review.
• Independent verification and release quality. Establish automated tests, AI evaluation suites, regression checks, and end-to-end acceptance tests. Reproduce failures, investigate root causes, and demonstrate fixes with evidence. Establish effective code review and quality gates without relying on the founder as the default reviewer.
• Reliable data and production systems. Turn imperfect source data into validated, traceable, versioned datasets and services. Own deployment, observability, error handling, recovery, latency, and operating costs.
• Security and responsible implementation. Build appropriate access controls, sensitive-data handling, audit trails, and safeguards. Work with domain experts to translate professional requirements into testable engineering behavior.

• Team leadership and execution. Break work into achievable milestones, delegate clearly, review critical changes, mentor engineers, and resolve integration blockers. Communicate progress, risks, and trade-offs early—with working demonstrations and honest quality results.

• What We Are Looking For10+ years of professional software engineering experience, including 4+ years owning architecture and technical decisions for production systems and 2+ years leading or mentoring engineers.
• Demonstrated full-stack ownership. You have personally delivered complete products across APIs, databases, user interfaces, integrations, and cloud infrastructure—not only contributed to a narrow layer.
• Production AI experience. You have shipped at least two AI/LLM-powered products or substantial features into real use. You can explain the reliability, evaluation, latency, cost, and operational trade-offs you encountered.
• Strong implementation skills. Expert-level Python, strong React/TypeScript, solid SQL and data-modeling skills, and hands-on experience with cloud deployment, CI/CD, and security fundamentals.
• Evidence of self-verification. You can show or explain a test or evaluation system you built, the failures it caught, and how you determined that a release was ready.
• Practical AI-native development experience. You actively use tools su…