This role is for one of Weekday’s clients

Min Experience: 6+ years
Location: Hyderabad, Telangana, India
JobType: full-time

On a four-person team, that means setting architecture and writing the load-bearing code. This is a hands-on leadership role—not a manager who delegates difficult problems.

You own the parts of the system where mistakes are expensive or irreversible:

● The data reconciliation engine that powers core workflows.

● The AI grounding layer that must never invent business numbers.

● The integrations that bring dealership data into the platform.

● Multi-tenant isolation, security, and auditability.

● The standards for AI-native software development.

You'll spend time inside dealerships, work closely with customers, and build systems grounded in operational reality—not assumptions.

Over time, you'll help shape engineering culture, support technical hiring, and grow into broader leadership responsibilities as the team scales.

Requirements

What You'll Own

Data Access & Integrations

● Build lawful, reliable, and version-stable mechanisms to extract data from existing systems.

● Work with customers and upstream system owners to solve integration challenges.

● Handle heterogeneous schemas and data formats.

● Create configuration-driven onboarding instead of code-driven implementations.

The Reconciliation Engine

● Design confidence-based reconciliation across multiple source systems.

● Detect confidently wrong matches—not just unmatched records.

● Own exception handling, auditing, and measurable accuracy.

The AI Grounding Layer

● Build constrained-query systems over free-form generation.

● Implement RAG, trace enforcement, and explainable outputs.

● Ensure every business number is verifiable.

● Treat "I can't answer that" as a feature, not a failure.

Multi-Tenant Foundations

● PostgreSQL Row-Level Security as the primary isolation mechanism.

● Cross-tenant leakage testing in CI.

● RBAC, audit trails, and data-protection foundations.

Event-Driven Architecture

● Celery-based event flows.

● Canonical data models.

● Configurable integration patterns that keep onboarding a new dealer are a matter of configuration, not code.

AI-Native Engineering Practices

● Define how the team uses AI coding agents.

● Establish review standards and guardrails for AI-generated code.

● Increase engineering leverage without compromising quality, correctness, or security.

Technical Leadership

● Set conventions for code structure, API design, and testing.

● Support junior engineers in owning features end-to-end.

● Help shape engineering culture and future hiring practices.

What We're Looking For

● Around 8 years of experience building production systems.

● Deep expertise in Python, Django, DRF, Celery, and PostgreSQL.

● Experience managing ORM migrations under changing schemas.

● Strong experience integrating messy external systems and reconciling data.

● Practical LLM and RAG experience, with healthy skepticism toward model outputs.

● PostgreSQL depth, including Row-Level Security, performance optimization, and multi-tenant patterns.

● Pragmatic thinking about scale—knowing when a modular monolith and simple queues are the right choices.

● Experience building with AI coding agents and establishing a team around them.

● The judgment to challenge unrealistic timelines, scope, or technical assumptions.

Nice to Have

● Fintech-grade or data-heavy reconciliation systems.

● Enterprise or legacy system integrations.

● Familiarity with ISO 27001, DPDPA, or related security practices.

● Working knowledge of Next.js and React.

You'll Thrive Here If

● You love ambiguity more than certainty.

● You prefer ownership over instructions.

● You think in systems, not tickets.

● You enjoy getting your hands dirty with customers.

● You learn by building rather than waiting.

● You use AI as leverage, not as a substitute for judgment.

● You want both the 0→1 and 1→100 journeys.

The Hard Truth

Early-stage startups are hard.

There will be ambiguity, changing priorities, and periods of intense execution because building from zero demands it.

But you'll own products end-to-end, work directly with founders and customers, and likely learn more in two years than many engineers do in ten.

Must-have skills

System architecture, LLM Guardrail, Data Pipelines

Good-to-have skills

LLM automation, Project planning, Stakeholder management