SDE 2

TheTakeoff.AISoftware Development Engineer (SDE II) - Backend & PlatformExperience: 3+ years | Location: Bangalore (In-person) | Type: Full-timeWho We AreTheTakeoff.AI is an AI-powered estimation engine built for industrial contractors. It reads P&ID drawings (Piping and Instrumentation Diagrams), extracts component counts, and generates material takeoffs that would otherwise take estimators hundreds of hours to produce by hand.The product targets specialty mechanical and petrochemical contractors on the US Gulf Coast, where a single refinery bid package can run 300+ P&IDs and a full piping takeoff can consume 875 estimator-hours. We automate that first pass so the estimator can verify and refine 5 to 10 times faster than starting from scratch.TheTakeoff.AI is built by ContraVault AI, a GenAI company founded by alumni of IIT-Delhi, NSUT, and XLRI Jamshedpur, with professional backgrounds spanning ITC, Microsoft, Nokia, GE, Alstom, ABB, and prior startups. The India-side platform already automates the tender and RFP lifecycle for enterprise teams. TheTakeoff.AI extends that document intelligence into industrial estimation for the US market.About the RoleWe're hiring an SDE II to build and own core parts of the backend powering an AI-first estimation product used by industrial contractors.You'll design and ship production services that process large volumes of complex engineering drawings, run agentic AI workflows, and stay reliable under enterprise workloads. The work spans API design, data modeling, distributed document processing, container orchestration on Kubernetes, and the runtime systems that make LLM-powered extraction fast, accurate, and cost-efficient.You'll work directly with the CTO and founding team, own architectural decisions end-to-end, and help set engineering standards as the team grows.Because the product sits at the intersection of AI and a specialized industrial domain, prior exposure to estimation workflows, engineering drawings, tenders, RFPs, or a process-heavy industry is a firm requirement. It shortens the path from requirement to correct implementation, and it makes your technical judgment sharper on the problems that matter most here.What You'll DoDesign and own backend services end-to-end: architecture, implementation, testing, deployment, and operation of production APIs using Python (FastAPI) and/or Node.js (Express)Build scalable document processing pipelines for ingestion, parsing, OCR, structured extraction, and enrichment across engineering drawings, P&IDs, isometrics, line lists, and specificationsEngineer the serving layer for AI features: retrieval services, RAG pipelines (Retrieval-Augmented Generation), vector search, caching, and response streamingArchitect asynchronous, event-driven systems: job queues, worker pools, retries, idempotency, and graceful degradation for long-running workloadsOwn data modeling and database performance: PostgreSQL schema design, indexing, query optimization, migrations, and partitioning, along with DynamoDB modeling where it fitsMake deliberate system design tradeoffs: latency budgets, throughput targets, cost per request, concurrency limits, and capacity planningBuild observability in from the start: structured logging, distributed tracing, metrics, alerting, and clear operational runbooksStrengthen platform security: authentication and authorization, multi-tenant isolation, audit trails, encryption, and access controlBuild agentic AI systems: design and operate agent loops covering planning, tool selection, execution, reflection, and termination, along with multi-agent handoffs and human-in-the-loop checkpointsEngineer the agent runtime: tool registries and schemas, short- and long-term memory, state machines and checkpointing for resumable runs, step budgets, loop detection, timeouts, and safe fallbacksMake agents observable and measurable: per-step tracing, token and cost accounting, trajectory replay, guardrails, and evaluation harnesses that score agent outcomes over timeOwn cloud infrastructure on AWS: S3, Lambda, ECS, IAM, and infrastructure as code, with safe and repeatable deploymentsRun workloads on Kubernetes: containerize services, manage deployments and rollouts on EKS, configure autoscaling for bursty AI workloads, and tune resource requests, limits, and node pools for cost and stabilityShip LLM-powered features with the AI team: prompt orchestration, structured extraction from engineering documents, retrieval tuning, and response quality benchmarkingRaise the engineering bar: thoughtful code reviews, testing strategy, CI/CD, technical documentation, and mentoring engineers and internsWhat You'll Bring3+ years of professional software engineering experience building and operating production backend systemsDomain experience with estimation, tenders, RFPs, or process-heavy industries (required): you've built software for, or worked directly within, workflows involving material takeoffs, quantity surveying, cost estimation, bid preparation, RFPs, RFQs, RFIs, EOIs, or similar solicitation and procurement processes. This can come from EPC (Engineering, Procurement, and Construction), industrial or process plant estimation, procurement or e-procurement platforms, bid and proposal management, contract lifecycle management, or compliance and eligibility evaluation. Time spent in a relevant industry counts equally: oil and gas, petrochemical, refining, energy and utilities, construction and infrastructure, defence, healthcare, manufacturing, logistics, or IT and consulting services. Experience with GovTech, LegalTech, ProcureTech, ConstructionTech, or supply chain products also qualifies. What matters is that you understand how these documents are structured, evaluated, and responded toA track record of shipping systems that real users depend on, and of keeping them healthy in productionStrong system design instincts and the ability to reason through tradeoffs such as latency versus cost, or consistency versus availabilityClean, well-tested, readable code and genuine care for long-term maintainabilityHigh ownership: you scope your own work, unblock yourself, and drive things to completionClear written and verbal communication, and comfort giving and receiving direct feedbackAdaptability in a fast-moving environment where priorities evolve quicklyPersistence: you see work through to the finishTechnical SkillsCore:Backend: Python (FastAPI) and/or Node.js (Express), with a solid grasp of async programming, concurrency, and performance characteristicsDatabases: PostgreSQL, including schema design, indexing, query planning, and performance tuning at scaleAPIs: REST API design, authentication and authorization, versioning, pagination, rate limiting, and idempotencySystem design: Distributed systems fundamentals, caching strategies, message queues, event-driven architecture, and horizontal scalingCloud: AWS (S3, Lambda, ECS, IAM), with working knowledge of deployment, networking, and cost managementContainers and orchestration: Docker, plus hands-on Kubernetes (deployments, services, ConfigMaps and secrets, health probes, resource limits, and rolling updates)Engineering practice: Git workflows, code review, automated testing (unit, integration, end-to-end), and CI/CDOperations: Diagnosing and resolving production issues across services, databases, and infrastructurePreferred:AI/LLM engineering: LLM APIs, prompt engineering, RAG pipelines, embeddings, and chunking strategiesVector search: pgvector or OpenSearch, including indexing strategies, hybrid search, and retrieval quality tuningAgentic AI systems: Agent loop design (plan, act, observe, reflect), tool-calling and function-calling pipelines, multi-agent orchestration, memory and context management, and Model Context Protocol (MCP)Agent frameworks: LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, or equivalent, along with durable execution engines such as Temporal or AWS Step FunctionsAgent reliability: Guardrails, output validation, retry and repair strategies, cost and step budgeting, sandboxed tool execution, and trajectory evaluationKubernetes at depth: EKS, Helm, HPA/KEDA autoscaling, ingress and networking, StatefulSets, Jobs and CronJobs for batch AI workloads, and GitOps with ArgoCD or FluxNoSQL: DynamoDB data modeling and access-pattern designDocument processing: PDF parsing, OCR, table extraction, layout-aware extraction, and engineering drawing interpretationDomain tooling: Estimation and takeoff software (Accubid, FastPIPE, PlanSwift, Bluebeam, or equivalent), e-procurement and tender portals (GeM, CPPP, SAM.gov, TED, or equivalent), contract lifecycle management systems, bid evaluation and scoring engines, or automated compliance and eligibility checkingInfrastructure as code: Terraform or AWS CDKHow We WorkSmall, senior team with direct access to the foundersFast decision-making and short feedback loops from idea to productionWritten clarity: design docs, considered code reviews, and shared contextOwnership over outcomes, with the autonomy to choose how you get thereInterview ProcessIntro call: your background, and what you want to build nextTechnical deep dive: a walkthrough of a system you've built end-to-endPractical exercise: a scoped, realistic engineering problemSystem design discussion: architecture for an agentic, document-heavy AI pipeline running at scaleFounder conversation: working style, ownership, and mutual fitWhat We OfferCompensation as per market standardsMeaningful ownership of core platform architectureFlexible working hoursIn-person: BangaloreDirect mentorship from and collaboration with the founding teamHard, high-impact problems in document intelligence and applied AI