Principal Engineer

Uplers; Bengaluru; Hybrid; INR 6,000,000 - 9,000,000; Full time

Job summary

Uplers in Bengaluru is seeking a Principal Engineer to define architecture and standards for Lighthouse and the production AI systems built on it. You will lead architectural coherence, write critical code, and shape how data, permissions, and workflows weave into enterprise AI solutions.

You will mentor engineers, drive security and observability, and ensure durable contracts across multiple services while collaborating with product and customers. 7+ years of deep tech leadership are expected.

Qualifications

• 7+ years of production software experience.
• Deep knowledge of Python, API/service design, and relational data modeling.
• Experience building enterprise AI/ML systems in production.

Responsibilities

• Define architecture and engineering standards for Lighthouse and AI systems.
• Write and review code on high-risk paths.
• Lead contracts and boundaries for data, permissions, and workflows.
• Mentor engineers and communicate decisions to stakeholders.
• Ensure security, observability, and compliant deployment.

Skills

Data Platforms

Docker/Kubernetes

FastAPI/Pydantic

AI/ML

API Design

Relational Modeling

Service Design

Azure

Cloud (GCP/AWS)

PostgreSQL

Python

Job description

Experience: 7.00 + years

Salary: Confidential (based on experience)

Expected Notice Period: 15 Days

Shift: (GMT+05:30) Asia/Kolkata (IST)

Opportunity Type: Hybrid ()

Placement Type: Full Time Permanent position(Payroll and Compliance to be managed by: Z1)

(*Note: This is a requirement for one of Uplers' client - Z1)

What do you need for this opportunity?

Must have skills required: Data Platforms, Docker/Kubernetes, FastAPI/Pydantic, AI, API, ML, Relational Data Modeling, Service Design, Azure, Cloud Server (Google / AWS), GCP, PostgreSQL, Python

Z1 is Looking for:

Principal Engineer

Define the architecture and engineering standards for Lighthouse and the production AI systems built on it.

We build enterprise AI systems around important business workflows. We bring together models, enterprise data, business context, applications, workflows, and human decisions so the result is secure, observable, governed, measurable, and useful in day-to-day operations.

Lighthouse is the platform accelerator beneath this work. Its independent Context Layer standardizes business meaning, mappings, permissions, evidence, and approved access paths between enterprise data and AI applications, while the wider platform provides connectivity, workflow orchestration, evaluation, governance, and operations. We turn recurring patterns into reusable capabilities without forcing a customer’s data stack, model provider, or workflow into a

generic template.

THE ROLE

As Principal Engineer, you will be the senior technical leader responsible for architectural coherence across Lighthouse and the systems it accelerates. This is not an architecture-only role: you will write and review code on the highest-risk paths, define durable contracts and boundaries, and lead decisions where enterprise data, permissions, workflow state, models, and customer-controlled infrastructure meet.

What You Will Own

• Set and evolve the reference architecture across the Lighthouse control plane, Context Layer, data and execution adapters, workflow and agent runtime, evaluation, and production operations.
• Define the contracts that keep the platform portable: canonical business concepts and tenant mappings reusable Context Modules and task Recipes context requests, plans, packages, and traces and the runtime boundary between context compilation and data execution.
• Own security and deployment architecture for shared, dedicated, and customer-hosted environments, including identity, authorization, tenant isolation, secrets, network boundaries, data residency, audit, and policy enforcement before retrieval and action.
• Design reliable execution for deterministic and model-driven workflows, including long-running state, approvals, retries, idempotency, timeouts, partial failure, compensation, safe replay, and rollback.
• Establish engineering standards for AI quality and system operations: evaluation datasets, regression gates, tracing, evidence and grounding, service-level objectives, incident learning, latency and cost budgets, and model or tool fallback.
• Make build-versus-buy and open-source extension decisions with clear upgrade paths turn proven patterns into versioned services, APIs, connectors, SDKs, and solution templates rather than premature abstractions or customer-specific forks.
• Personally design, implement, and review the components with the greatest architectural, security, performance, or operational risk mentor engineers and communicate decisions clearly to product, business, and customer stakeholders.

What Success Looks Like

• Lighthouse has clear system boundaries and stable contracts, so new use cases reuse platform capabilities without creating hidden coupling or one-off forks.
• Context is permission-aware, bounded, testable, and explainable: the system can show which business definitions, sources, policies, freshness checks, and evidence supported an answer or action.
• Production workflows degrade safely and recover predictably, with measurable reliability, quality, latency, cost, and business outcomes.
• The engineering team moves faster because high-impact decisions are documented, risks are surfaced early, ownership is clear, and standards are enforced through code and tooling.

What You Bring

• 8+ years building production software, including principal-level technical leadership across distributed, data-intensive, platform, or enterprise systems.
• Deep hands-on strength in Python, API and service design, relational data modeling, asynchronous and event-driven systems, and cloud-native operations.
• Experience defining architecture across multiple services and deployment modes, including identity, authorization, multi-tenancy or customer isolation, observability, and failure recovery.
• Experience taking AI or ML systems beyond prototypes, including structured model outputs, tool use, retrieval or context, evaluation, guardrails, and production debugging.
• Strong judgment about system boundaries, data ownership, consistency, scalability, security, build- versus-buy choices, and how much abstraction is justified at each stage.
• A track record of leading through influence while staying close to implementation, code quality, production behavior, delivery sequencing, and customer outcomes.

Useful, But Not Required

• Experience with enterprise metadata, semantic or knowledge-graph systems, workflow engines, data platforms, developer platforms, or multi-tenant SaaS.
• Familiarity with parts of our likely stack: FastAPI/Pydantic, PostgreSQL, Redis, queues or event systems, MCP, vector or graph stores, Docker/Kubernetes, and AWS, Azure, or GCP.
• Experience integrating or extending open-source platforms while preserving upgrade paths, or deploying into regulated, private-network, or customer-controlled environments.

Why Us

• Work at the intersection of enterprise data, business semantics, AI, workflows, security, and human decision-making.
• Shape a platform while its most important contracts and operating model are still being defined.
• Build technology that becomes part of how enterprises operate, rather than isolated demonstrations or thin model wrappers.
• Join a small, ambitious team where strong technical judgment, direct implementation, and end-to- end ownership have visible impact.