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Director of Engineering — Flights Platform
Travel Tech - IPO company
Director of Engineering — Flights Platform

Director of Engineering — Flights Platform at Travel Tech - IPO company · Bengaluru (Bangalore) · 12 - 16 years · ₹80L - ₹130L / yr · Posted 13 May 2026

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Director of Engineering — Flights Platform

at Travel Tech - IPO company

Agency job
12 - 16 yrs
₹80L - ₹130L / yr
Bengaluru (Bangalore)
Skills
Distributed Systems
Search systems
Pricing & Fare Engine
Booking & Ticketing
Airline Integrations
Post-Booking Flows
Latency
Accuracy
High availability
Consistency
Performance tuning
Performance management
Performance Testing
Scalability
Large Language Models (LLM)
Retrieval Augmented Generation (RAG)
Model Context Protocol (MCP)
Agentic AI
Workflow management
Workflow optimization
Reliability
Observability
skill iconJava
skill iconGo Programming (Golang)
skill iconPython
Microservices
Apache Kafka
grpc
skill iconRedis
Aerospike
DynamoDB
skill iconElastic Search
skill iconAmazon Web Services (AWS)
Amazon EKS
Amazon EC2
Amazon S3
prometheus
skill icongrafana
Engineering Management
Software engineering
Stakeholder management
Team Management
Team building
B2C
B2B
Travel Tech
FinTech
Consumer
Real-time systems
market place dynamics
API
API management

Director of Engineering — Flights Platform

AI-First Travel Commerce · High-Scale Distributed Systems · Marketplace Infrastructure


🌏 The Problem Space

A flight search looks trivially simple. It is anything but.


Every query you fire triggers a choreography of distributed systems operating in real-time — integrating with a dozen airline GDS/NDC providers, computing dynamic fares across inventory buckets and fare rules, ranking thousands of itineraries by relevance and business intent, and returning a ranked, priced, bookable result set — all in under 100ms.


→ Millions of search queries per minute

→ <100ms end-to-end SLA with external API dependencies

→ High-value transactions — a bug here means a missed booking, not a failed render

→ Pricing errors erode trust faster than any other failure mode


We are rebuilding the Flights platform as a real-time commerce engine for Bharat — AI-native from day zero, built to power both B2C consumer journeys and high-stakes B2B enterprise corridors.


This is a once-in-a-decade opportunity to build national-scale flight infrastructure from first principles.

🧠 What You Will Own

You will own the full Flights platform — systems, architecture, and the teams that build them.


Core System Domains:

•Search Systems — high-throughput, low-latency query pipelines returning ranked, bookable options

•Pricing & Fare Engine — dynamic pricing logic, fare rules, promotional overlays, and real-time validation

•Booking & Ticketing — transaction-critical flows requiring strict consistency, idempotency, and zero data loss

•Airline Integrations — managing unreliable external GDS/NDC APIs with retries, circuit-breakers, and reconciliation

•Post-Booking Flows — cancellations, modifications, refunds — correctness at the margin is non-negotiable


Platform Scope:

•High-scale APIs serving consumer apps, B2B enterprise clients, and third-party partners

•Event-driven state machines managing booking workflows across async boundaries

•Observability and reliability infrastructure across all mission-critical flows


Team Scope:

•Lead 15–30+ engineers across multiple product and platform teams

•Manage Engineering Managers and Principal/Staff engineers

•Own hiring, org design, and technical direction


⚙️ Core Engineering Challenges

This role is fundamentally about making the right trade-offs under uncertainty — at scale.


Latency vs. Accuracy — when do you serve a cached fare vs. call a live airline API?

Availability vs. Consistency — graceful degradation at booking time vs. strict price validation

Cost vs. Performance — when is an external API call worth it vs. a cache hit?

Scalability vs. Simplicity — the best system is the one your team can reason about under incident


🤖 AI-First Engineering

AI is not an afterthought. It is load-bearing architecture.

•LLM-powered pricing intelligence — dynamic fare prediction and demand signals

•RAG pipelines for fare rules, refund policy, and support automation

•Agentic booking resolution workflows — autonomous exception handling at scale

•MCP-based orchestration layers for multi-provider integration


⚖️ Key Responsibilities

Architecture & Distributed Systems

•Design and evolve sub-100ms distributed query systems serving millions of concurrent searches

•Build fault-tolerant booking pipelines with strong consistency and durability guarantees

•Drive Kafka-based event architectures for booking state management


Reliability & Observability

•Own 99.99%+ availability for booking and pricing systems

•Build deep observability — metrics, distributed tracing, structured logging, SLOs/SLAs

•Lead post-incident reviews and drive systemic reliability improvements


Business Partnership

•Partner with Product, Revenue, and Partnerships to translate commercial goals into architecture

•Influence platform roadmap, supplier strategy, and long-term technical investment


🛠️ Technology Stack

Backend: Java · Kotlin · Go · Python

Architecture: Microservices · Event-Driven (Kafka) · gRPC

Data: Redis · Aerospike · DynamoDB · Elasticsearch

Cloud: AWS (EKS, EC2, S3)

Observability: Prometheus · Grafana · OpenTelemetry


👤 Who You Are

•12–16 years in backend/distributed systems; 5+ Years in an Engineering Leadership role, led teams of 15–50 engineers

•Built and scaled large B2C + B2B platforms — Travel Tech, FinTech, or high-scale Consumer

•Deep expertise in real-time systems, marketplace dynamics, and external API integration

•Tier-I institute background strongly preferred (IIT / IIIT / NIT / IISC / BITS / VIT / SRM — CSE/ISE)


🚀 Why This Matters

Build national-scale infrastructure for 1.4 billion people

Sit at the intersection of AI · distributed systems · marketplace economics

Define the future of travel commerce in India — from architecture to product



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Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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We are looking for a Director of Engineering to lead engineering execution and delivery across our enterprise AI solutions. This role combines strong engineering leadership with operational discipline, ensuring AI products move from technical design to production with predictable timelines, high quality, and reliable performance.

You will lead Engineering Managers and Tech Leads, oversee multiple engineering teams, and drive delivery excellence across AI-powered applications and platforms. The focus is on execution, technical quality, and organizational accountability, ensuring teams deliver scalable, production-ready AI systems without compromising reliability or engineering standards.

Key Responsibilities

1. Engineering Delivery & Execution

  • Own engineering delivery schedules, translating product roadmaps into achievable sprint plans and release milestones.
  • Establish measurable delivery targets, aiming for at least 85% on-time delivery across engineering teams.
  • Identify and resolve cross-team dependencies, technical bottlenecks, and resource constraints before they affect delivery.
  • Drive engineering process improvements across Agile development, CI/CD, automated testing, and release management.
  • Monitor delivery metrics, sprint commitments, and release readiness to maintain predictable execution.

2. AI Quality, Evaluation & Governance

  • Establish automated AI evaluation pipelines to measure hallucinations, relevance, groundedness, and contextual accuracy before production releases.
  • Define quality benchmarks and evaluation criteria for LLM-powered applications and AI-driven workflows.
  • Implement safety mechanisms, validation filters, circuit breakers, and human-in-the-loop fallbacks to manage unpredictable AI outputs.
  • Oversee RAG pipelines, vector databases, data ingestion, and retrieval mechanisms to maintain accurate, reliable, and compliant context.
  • Reduce production AI quality regressions through continuous evaluation, monitoring, and feedback loops.

3. Architecture, Scalability & Reliability

  • Oversee the architecture of distributed systems and cloud-native applications to support scalability, availability, and performance.
  • Drive reliability targets, including 99.9% uptime and low time-to-first-token (TTFT) latency for applicable AI applications.
  • Partner with Staff and Principal Engineers to design resilient systems, including fallback mechanisms for model API failures, timeouts, and rate limits.
  • Establish engineering standards for code reviews, observability, automated integration testing, and security compliance.
  • Ensure production systems meet defined service-level objectives (SLOs) and performance benchmarks.

4. Engineering Leadership & Team Development

  • Lead and develop Engineering Managers and Tech Leads, establishing clear ownership, accountability, and delivery expectations.
  • Build a high-performance engineering culture focused on technical excellence, collaboration, and continuous improvement.
  • Partner with Talent Acquisition to attract, assess, and hire strong software engineering and AI talent.
  • Align engineering resources with business priorities, delivery commitments, and technical requirements.
  • Address performance and execution challenges while maintaining team engagement and engineering quality.

Success Metrics: First 90 Days

First 30 Days

  • Assess existing engineering workflows, delivery pipelines, and release processes.
  • Establish baseline AI evaluation criteria and identify gaps in production quality.
  • Take ownership of engineering delivery across teams and align expectations with Engineering Managers and Tech Leads.

First 60 Days

  • Implement automated AI evaluation frameworks and establish measurable quality benchmarks.
  • Improve cross-team coordination and resolve key delivery dependencies.
  • Establish consistent sprint planning, release cadences, and delivery reporting.

First 90 Days

  • Achieve at least 85% sprint commitment completion.
  • Demonstrate measurable reductions in production AI quality regressions.
  • Improve system reliability and stabilize latency against defined performance targets.
  • Establish clear accountability and predictable delivery practices across engineering teams.

Qualifications & Requirements

  • Experience: 14+ years in software engineering, including at least 5 years managing Engineering Managers or Tech Leads in enterprise or high-growth environments.
  • AI/ML Expertise: Hands-on experience delivering AI-powered systems involving LLMs, RAG architectures, vector databases, prompt engineering, or model fine-tuning workflows.
  • Engineering Delivery: Proven ability to lead multiple engineering teams and deliver complex technical initiatives from design through production launch.
  • System Architecture: Experience with distributed systems, cloud-native backend infrastructure, and scalable APIs across AWS, GCP, or Azure.
  • AI Quality & Reliability: Understanding of AI evaluation frameworks, production monitoring, hallucination mitigation, guardrails, and system reliability.
  • Delivery Management: Strong experience with Agile methodologies, CI/CD, release planning, and engineering performance metrics.
  • Leadership: Ability to establish accountability, manage competing priorities, and deliver against ambitious deadlines without compromising quality or team morale.

What Success Looks Like

The successful candidate will establish predictable engineering delivery, improve the quality and reliability of production AI systems, and build a culture of ownership across engineering teams. They will combine technical judgment with execution-focused leadership to ensure AI solutions are delivered on schedule, operate reliably, and meet enterprise expectations.

If you are passionate about building high-performing engineering teams and delivering enterprise-grade AI solutions at scale, we would like to hear from you.

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Blitzy
at Blitzy
2 candid answers
1 product
Taher Ujjainwala
Posted by Taher Ujjainwala
Pune
12 - 25 yrs
₹70L - ₹120L / yr (ESOP available)
skill iconPython
skill iconKubernetes
Google Cloud Platform (GCP)
skill iconAmazon Web Services (AWS)
Windows Azure
+15 more

About the Role

We are hiring Staff / Principal Engineers to take full, hands-on ownership of Blitzy's most critical production-grade systems and to deliver high-leverage features that materially improve customer outcomes and engineering velocity. This is the most senior individual contributor role at the company today.

This is not a Senior-plus role, an architecture-only role, or a promotion-track role. We are looking for someone who has already operated at Principal / Staff+ scope in a highly technical environment and expects to spend their time writing, reviewing, and shipping production code.

This role is 100% hands-on. Leverage comes from system ownership, execution quality, and durable technical decisions — not people management or process.


Responsibilities

  • Own mission-critical production systems end-to-end, ensuring correctness, scalability, performance, reliability, and operational excellence.
  • Design, build, and ship high-impact backend systems and features that improve product reliability, performance, and customer value.
  • Architect scalable services and cloud infrastructure using technologies such as Python, REST, gRPC, Kubernetes, and Terraform.
  • Identify and resolve complex technical bottlenecks that limit engineering quality, system performance, or organizational velocity.
  • Build and operate LLM-powered systems and validation loops that evaluate correctness, consistency, durability, and production performance.
  • Design and evolve data architectures incorporating relational, NoSQL, graph, and vector databases to support complex enterprise applications and semantic retrieval.
  • Modernize and improve complex enterprise systems while balancing reliability, maintainability, scalability, and delivery speed.
  • Set and uphold engineering quality standards through hands-on technical leadership, sound technical judgment, and ownership of long-term technical decisions.


Qualifications

  • Direct experience with Python as a primary programming language, backend frameworks, and microservices architectures.
  • Expertise in REST and gRPC, with proficiency in Node.js and JavaScript.
  • Proficiency in GCP, along with experience using at least one additional cloud platform such as AWS or Azure.
  • Advanced knowledge of Kubernetes and Terraform in production environments.
  • Experience operating highly available production systems, including monitoring, scalability, reliability, performance optimization, and operational tooling.
  • Strong knowledge of SQL and NoSQL databases, including PostgreSQL, MySQL, MongoDB, Cassandra, or DynamoDB.
  • Familiarity with graph databases such as Neo4j and vector databases or embedding infrastructure for semantic search and retrieval.
  • Hands-on experience building and operating LLM-powered systems in production, including evaluation, validation, regression testing, tracing, and failure analysis.
  • Working knowledge of LangSmith or comparable LLM observability and evaluation tools; familiarity with OpenAI, Anthropic, or similar model providers is a plus.
  • Ability to contribute across the full stack, with a strong understanding of frontend architecture and the ability to debug, design, and ship across frontend, backend, infrastructure, and AI systems.
  • Understanding of large-scale enterprise software systems, including architecture, integration, deployment, modernization, and long-term maintainability.
  • Proven track record of operating at Staff+, Principal Engineer, or equivalent level, independently driving complex technical initiatives and delivering high-impact outcomes with minimal supervision.


Blitzy is a Cambridge, MA based AI software development platform on a mission to revolutionize the software development life cycle by autonomously building custom software to unlock the next industrial revolution. We're transforming how enterprises build software, turning enterprise requirements into enterprise grade code with an agentic software development platform that can autonomously execute 80% of the quantum of software development work. We're backed by multiple tier 1 investors, and have proven success as founders of previous start-ups.


Our Culture

Who we are:

Led by two pioneering co-founders we are one of the fastest growing companies in the U.S., creating our own category of enterprise autonomous software development. We automate thousands of hours of software development for our customers, which includes strong representation within the Fortune 500.


How we work:

  • We move Blitzy Fast: Time is both our company’s and our clients’ most precious asset. We move quickly and decisively to innovate internally and deliver exceptional software externally.
  • Championship Mindset: We operate like a professional sports team. We win as a team by holding ourselves and each other to high standards, collaborating in-person, and remaining focused on the mission.
  • Passion for Invention: We’re pushing the frontier of what’s possible, requiring constant innovation and iteration.
  • We Work for the Customer: We focus on delivering outsized value to the customers we work with and expanding those relationships into deep, meaningful partnerships.
  • We believe in being ‘everyday athletes’: taking care of ourselves so we can bring our best minds to work. We promote great sleep, movement, and restorative activities for 


Blitzy is an equal opportunity employer committed to building a diverse and inclusive team. We believe different perspectives make us stronger.

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