J&F

Technical Project Manager.

Run delivery for 15–25 backend, frontend, and AI engineers building construction

tech and CAD drawing-automation software — with structural engineers on one

side of you and the codebase on the other.

FULL-TIME

MANAGER · 7+ YRS

Engineering — Delivery & Program Management

TEAM YOU’LL RUN

15–25 engineers · Backend · Frontend · AI

LOCATION

India

EXPERIENCE

6–7 yrs min · 4+ yrs managing engineers

WORK MODE 

Hybrid

DOMAIN

ENGAGEMENT

AEC · CAD / BIM automation · Multi-tenant SaaS

About BuildTwin

BuildTwin is a multi-tenant SaaS platform that runs the day-to-day operations of construction

and infrastructure companies — their workforce, projects, commercials, and documents — on a

single system. Alongside it we build an AI drawing-automation platform that turns structured

element data into production-ready technical drawings and exports them to PDF and DXF.

Both products are built by a small, senior engineering group in India: backend engineers on an

AWS serverless platform, frontend engineers on Angular and React, and AI engineers working

on drawing generation and data extraction. Sitting next to them — not in another building, not

in another timezone — are the structural and detailing engineers who define what a correct

drawing actually is.

That adjacency is our biggest advantage and our hardest coordination problem. This role exists

to make it work.

 Role overview

We are hiring one Technical Project Manager to own delivery across our engineering teams in

Bengaluru. You will run 15–25 engineers across backend, frontend, and AI workstreams, and

you will be the person who turns requirements originating with our structural and detailing

engineers into scoped, sequenced, shipped software.

This is a technical role. You will not be handed a groomed backlog and asked to move cards

across it. You will sit in the design discussion and have a view on how and when — you will

push back on an approach that will not scale, ask why a trade-off was made, and know the

difference between a two-day change and a two-sprint one. You are not expected to write

production code. You are expected to read it, understand the system, and never be the least

informed person in a technical conversation you are chairing.

The centre of gravity of this job is the floor, not the calendar. Most of your day is

spent with the people building and the people specifying — closing the gap between what a

domain expert means and what an engineer can implement deterministically. Status

reporting is a by-product of doing that well, not the job itself.

What you’ll own.

 Delivery ownership

Own the end-to-end delivery plan for every active workstream — scope, sequence,

dependencies, capacity, and dates that actually hold.

Break large, ambiguous product intent into engineering-sized increments with explicit

acceptance criteria, agreed with the people who will build them.

Run the operating cadence: planning, standups, refinement, demos, and retrospectives that

change something the following week.

Maintain one honest view of status — on track, at risk, slipped, and why — visible to the

founders and to the team at the same time.

Manage cross-team dependencies between backend, frontend, AI, and drawing-automation

workstreams so no team idles waiting on another.

Surface risk early, quantify it, and arrive with options — never an escalation without a

proposal.

Protect the team from thrash: absorb changing priorities, re-plan properly, and say no or not

now when a plan cannot take more.

Drive releases to genuinely done — QA, UAT with domain experts, documentation, and post

release verification included.

Own and improve the delivery metrics that matter: cycle time, predictability of committed

scope, defect escape rate, and rework.

Run the release calendar across two products so neither becomes the permanent second

priority.

Technical partnership

Participate substantively in technical discussions on design, sequencing, and trade-offs —

and hold your own with senior engineers.

Understand the architecture well enough to reason about blast radius: what a schema

change touches, why a migration is risky, why an API contract must be agreed before two

teams start.

Challenge estimates that look wrong in either direction, and recognise when an estimate is

uncertain because the problem is still unclear.

Translate technical constraints into decisions leadership can act on, and business constraints

into direction engineers can act on.

Keep testing, observability, and debt-reduction work on the plan instead of losing to features

every single quarter.

Insist that decisions get written down — architecture choices, drawing rules, API contracts,

and the reasoning behind each.

Stay close to the artefacts: read the tickets, the design docs, the PR titles, and the incident

write-ups.

Chair technical discussions where the outcome is a decision with an owner and a date, not a

longer discussion.

Run delivery for 15–25 backend, frontend, and AI engineers building construction- tech and CAD drawing-automation software — with structural engineers on one side of you and the codebase on the other. Must have Hands-on familiarity with AutoCAD, Revit or experience at a product company in the CAD / AutoCAD / BIM / PLM / EDA /simulation space

Role overview-

We are hiring one Technical Product Manager to own delivery across our engineering teams in Bengaluru. You will run 15–25 engineers across backend, frontend, and AI workstreams, and you will be the person who turns requirements originating with our structural and detailing engineers into scoped, sequenced, shipped software.

This is a technical role. You will not be handed a groomed backlog and asked to move cards across it. You will sit in the design discussion and have a view on how and when — you will push back on an approach that will not scale, ask why a trade-off was made, and know the

difference between a two-day change and a two-sprint one. You are not expected to write production code. You are expected to read it, understand the system, and never be the least- informed person in a technical conversation you are chairing

What you’ll own- Delivery ownership

• Own the end-to-end delivery plan for every active workstream — scope, sequence,dependencies, capacity, and dates that actually hold.
• Break large, ambiguous product intent into engineering-sized increments with explicit acceptance criteria, agreed with the people who will build them.
• Run the operating cadence: planning, standups, refinement, demos, and retrospectives that change something the following week.
• Maintain one honest view of status — on track, at risk, slipped, and why — visible to the founders and to the team at the same time.
• Manage cross-team dependencies between backend, frontend, AI, and drawing-automation workstreams so no team idles waiting on another.
• Surface risk early, quantify it, and arrive with options — never an escalation without a proposal.
• Protect the team from thrash: absorb changing priorities, re-plan properly, and say no or not now when a plan cannot take more.
• Drive releases to genuinely done — QA, UAT with domain experts, documentation, and post- release verification included.
• Own and improve the delivery metrics that matter: cycle time, predictability of committed scope, defect escape rate, and rework.
• Run the release calendar across two products so neither becomes the permanent second priority.

The floor & the team.

Our requirements do not come from a product spec written in a vacuum. They come from structural and detailing engineers sitting on the floor with decades of practice behind them. Converting that into software is the defining skill of this job.

• Partner with structural and detailing engineers to turn drawing standards, project practice, and domain expectation into unambiguous, implementable requirements
• Run requirement sessions whose output is a written spec — sample data, expected output,edge cases, and explicit non-goals — not a shared verbal understanding.
• Arbitrate the constant gap between “how it has always been done on site” and “what can be deterministically automated.”
• Set up validation loops so domain experts review generated output early and repeatedly, rather than at the end of a sprint.
• Maintain a decision log for domain rules so the same question is not re-litigated three sprints later by different people.
• Manage internal stakeholder expectations and, where relevant, commitments made to customers.
• Spot when a “small clarification” is actually a scope change, and handle it as one.
• Make sure engineers get access to the domain expert directly — your job is to structure that contact, not to become a relay in the middle of it.

Challenges you’ll solve.

We prefer to be candid. These are the problems that make this role genuinely difficult — and genuinely interesting.

Domain knowledge that arrives as tribal knowledge

Our structural and detailing engineers know what a correct drawing looks like, but much of that knowledge is tacit. Your job is to extract it into deterministic, testable rules before an engineer starts building. Getting this wrong is the single most expensive failure mode we have

— it produces work that looks finished and is not.

Three disciplines, one release

Backend (AWS serverless), frontend (Angular / React), and AI engineers ship into the same

product. They have different failure modes, different testing regimes, and different natural cadences. Sequencing them so nobody idles and nothing integrates late is the core scheduling problem here.

Correctness is not negotiable

This is construction software. A wrong drawing, a wrong quantity, a wrong permission, or a wrong payroll figure has consequences outside the screen. “Ship it and iterate” has limits here, and you will need judgement about exactly where those limits sit for each workstream.

AI workstreams do not estimate like CRUD

Drawing generation and extraction work is research-shaped: some weeks produce a breakthrough, some produce a negative result. You will plan around that uncertainty honestly— with timeboxes, decision points, and fallbacks — instead of pretending an unknown is a two-

week ticket.

Live customers, finite engineers

Companies run their operations on this platform daily. Production incidents, customer escalations, and enterprise integrations (Asite, Autodesk Construction Cloud) compete with roadmap work for exactly the same people. You will make that trade-off explicitly, every week,

and be able to defend it.

Two products, one organisation

The operations platform and the drawing-automation platform have different customers, different rhythms, and partly shared people. Keeping both moving — without either becoming the perpetual second priority — is a standing constraint on every plan you make.

Founder-adjacent, fast-changing priorities

Direction can change on new customer information, and sometimes it should. You are the shock absorber: re-plan quickly, communicate the change clearly and once, and make sure the team experiences it as a decision rather than as chaos.

Qualifications.

• 6–7 years minimum in software delivery, including at least 4 years directly managing engineering teams as a project, delivery, or engineering program manager.
• Proven experience running teams of 10+ engineers across more than one discipline —backend, frontend, mobile, data, or AI.
• Fluency in modern delivery practice — Agile / Scrum or Kanban applied with judgement rather than ceremony — plus genuine facility with Jira or Linear, Git workflows, CI/CD, and release management.

Strongly preferred

• Experience at a product company in the CAD / AutoCAD / BIM / PLM / EDA /simulation space — Autodesk, Bentley, Dassault, Siemens, PTC, Trimble, Hexagon, Ansys or similar.
• Exposure to AEC, construction tech, manufacturing, or industrial software where domain correctness matters more than interface polish.
• Experience managing teams that included AI / ML engineers alongside conventional product engineers.
• Hands-on familiarity with AutoCAD, Revit, Civil 3D, Tekla, or DXF / drawing-export workflows.

The bar, the process, the offer.

The manager we’re looking for

Technical enough to be trusted

Engineers respect the questions you ask, not just the dates you set.

Owns outcomes, not activity

Measures the job by what shipped and worked, not by how busy the board looked.

Direct and specific

Says the uncomfortable thing early, in plain language, to the person who needs to hear it.

Reduces chaos

Leaves every process, plan, and handoff simpler than they found it.

Holds opinions loosely

Strong views on how to run delivery, updated when the evidence changes.

Raises the standard around them

The team becomes more predictable and more capable because you are in it.

Job Description:

Position: CAD/CAM Developer / CAD Software Developer

Experience: 3–6 years

Employment Type: Full-time

Location: Hybrid/ Remote

Job Summary

We are looking for a skilled CAD/CAM Developer to design, develop, customize, and optimize CAD-related software applications and plugins. The ideal candidate should have strong programming experience in C++, C#, and/or Python, along with hands-on experience in CAD APIs, 2D/3D geometry, computational geometry, mathematical modeling, and algorithm development.

The candidate will work closely with engineering and product teams to develop high-performance CAD solutions, automate design workflows, process 3D geometry and point-cloud data, and improve the accuracy and efficiency of engineering applications.

Key Responsibilities

• Develop and maintain CAD/CAM software applications, plugins, and automation tools.
• Develop solutions using C++, C#, Python OR .NET.
• Work with CAD APIs such as AutoCAD API, Revit API, or equivalent CAD SDKs.
• Develop algorithms for 2D/3D geometry processing and geometric modeling.
• Design and optimize computational geometry algorithms for complex engineering problems.
• Work with 3D models, point-cloud data, mesh data, and spatial information.
• Implement geometric operations including alignment, rotation, transformation, measurement, and shape/geometry extraction.
• Develop tools for CAD model validation, quality checking, and automated workflows.
• Optimize algorithms for performance, accuracy, and scalability.
• Integrate software components and third-party SDKs into CAD applications.
• Participate in code reviews, debugging, testing, and technical documentation.
• Collaborate with cross-functional teams including engineering, product, and technical teams.
• Follow Agile development practices and maintain source code using Git/SVN.

Required Skills

Programming

• C++
• C#
• Python
• .NET Framework / .NET Core
• Object-Oriented Programming
• Data Structures & Algorithms

CAD / Geometry

• CAD/CAM Software Development
• AutoCAD / Revit / Autodesk platforms
• CAD API / SDK development
• 2D/3D Geometry
• Computational Geometry
• Geometric Modeling
• Spatial Computing
• Mathematical Modeling
• Algorithm Development & Optimization

3D / Point Cloud – Preferred

• Point Cloud Processing
• 3D Reconstruction
• Mesh Processing
• Voxelization
• PCL / Open3D
• LAS / LAZ / E57 / PLY formats
• Coordinate Transformation

Role Summary

We are hiring a Data Engineer / ML Data Pipeline Engineer to build and operate the data backbone of the Enterprise AI platform:

What You'll Own

• Ingestion & ETL/ELT pipelines for heterogeneous project folders (PDF drawings, SVG files, IFC models, BBS.json bar-bending-schedule data, Excel exports, and AI agent output JSON).
• AWS-based data architecture: S3 raw/staging/curated/outputs structuring, partitioning, versioning, and lifecycle management; querying via Athena/Glue and warehousing via Redshift or Snowflake as needed.
• Data validation frameworks: GUID cross-referencing between SVG and BBS data, schema enforcement, duplicate/orphan detection, reference integrity checks, and structured validation reporting.
• Agent run logging & observability: designing the database schema and pipelines that track every AI agent run (inputs, outputs, status, errors, cost, retries, reviewer feedback).
• AI Factory monitoring dashboards: operational dashboards (failure rates, retries, latency, data quality) and business dashboards (throughput, cost per run, rework rate) for Power BI/QuickSight or equivalent.
• ML data pipeline support: dataset preparation, labeling/annotation workflows, human-in-the-loop review tooling, and dataset versioning for models that classify or QC drawing issues.
• APIs: designing and building FastAPI/Flask endpoints to trigger validation runs and expose agent processing status to internal tools.
• Data quality & testing discipline: idempotent pipelines, quarantine/reject handling, regression and reconciliation testing, and root-cause debugging when pipelines or query performance degrade in production.

Key Skills — Non-Negotiable (Must-Have, Strong Level)

• Python — production-grade scripting: file/folder handling, JSON/schema processing, clean error handling, not just notebook-level scripting.
• SQL — strong hands-on ability, including GROUP BY/HAVING for duplicate detection, window functions, and daily aggregate/rate calculations (e.g., success-rate queries).
• AWS S3 data handling — practical experience structuring buckets for raw/staging/curated data, versioning, and avoiding overwrite issues at scale.
• Data validation — demonstrable experience building validation logic (set comparisons, duplicate/missing detection, structured pass/fail reporting), not just "I write assertions."
• ETL/ELT pipeline design — end-to-end ownership of at least one pipeline: source → transform → storage → validation → monitoring → business outcome, with clear articulation of what they personally built.
• Query/warehouse engine judgment — working knowledge of when to use Athena vs. Redshift vs. Snowflake (or equivalent), partitioning, clustering, sort/distribution keys, and storage format trade-offs (Parquet vs. JSON vs. CSV).

Key Skills — Good to Have

• Dashboarding — Power BI / QuickSight (or equivalent) fact/dimension table design, KPI cards, drill-downs; medium-to-strong level is a plus but trainable.
• FastAPI / Flask — building real endpoints with request/response schemas and basic error handling; especially valuable for validation-trigger and agent-status APIs.
• ML data pipeline experience — dataset labeling, annotation platform design, train/test/validation splitting, dataset versioning; strong on the pipeline/data side rather than model training itself.
• Human-in-the-loop / review tooling — experience building or contributing to browser-based labeling/review platforms (session persistence, label schema, export formats).
• Large-scale metadata querying — experience making file discovery fast across large volumes (1,000+ projects, thousands of files each) via metadata index tables, event-based ingestion, or catalog tools like AWS Glue.

Role overview:

We are hiring one Senior Backend Engineer to take end-to-end ownership of our serverless backend — a hands-on IC role for someone both technically excellent and comfortable being one of the few people the entire backend depends on. You'll own the services across several Node.js and Python repositories, work directly with the founders and product team, and set the technical bar for reliability, security, and performance.

Key responsibilities

• Design, build, and operate AWS Lambda services across our HCM/workforce, project-management, commercial/revenue, permissions, and document domains — each comprising dozens of functions.
• Own the multi-tenant PostgreSQL data layer — schema design, query performance, and the permission/relationship model — end to end.
• Maintain and evolve the request path — API Gateway → custom Lambda authorizer → VPC-bound Lambda → private databases — including the runtime IAM/credential model that scopes every request.
• Safeguard tenant isolation and security across a per-company Cognito authentication model.
• Build and maintain integrations with external construction data environments (Asite, Autodesk Construction Cloud), including large-scale document synchronization.
• Optimize performance and reliability to keep latency-sensitive endpoints well within platform limits under growing load.
• Raise the engineering bar — testing, observability, CI/CD, and modernization of legacy components.
• Debug and resolve production incidents to root cause, and put safeguards in place so they don't recur.
• Document decisions and designs and collaborate with the frontend (Angular) and product teams.

Challenges you'll solve.

We prefer to be candid — these are the problems that make this role genuinely interesting:

Latency under a hard ceiling

API Gateway terminates any request beyond ~29 seconds regardless of the Lambda's own timeout — yet much of our value comes from heavy cross-project reporting. You'll keep p95 latency within budget through set-based SQL, pagination, streaming, and asynchronous processing.

Least-privilege, per-request security

A shared custom authorizer mints short-lived, request-scoped credentials via sts:AssumeRole under a strict 2,048-character inline session-policy limit. You'll design permission models that stay within that budget and reason about IAM precisely.

Graph-shaped data, relational store

The permission and relationship model is inherently graph-like, but lives in PostgreSQL — you'll model it with recursive queries, careful indexing, and set-based traversal rather than reaching for a separate graph engine.

Watertight multi-tenancy

One Cognito pool per company and tenant-scoped access throughout — isolation is a first-order concern.

VPC-bound serverless

Lambdas run inside a VPC to reach private databases; you'll manage cold starts, connection lifecycles, and pool limits.

Resilient external integrations

Syncing large document sets from third-party APIs (including SOAP/XML) demands backpressure, deduplication, retries, and graceful partial-failure handling.

Compute-heavy workloads

Server-side PDF generation, image processing, and multi-currency handling within Lambda's memory and time constraints.

The stack.

Runtime — Node.js, Python, AWS Lambda

AWS services — -1 API Gateway, Lambda, Cognito, STS / IAM, Secrets Manager, S3 CloudWatch, VPC, EC2

Infrastructure & CI/CD- AWS SAM, CodePipeline → CodeBuild Shared Data —PostgreSQL

Qualifications.

• 5+ years building and operating production backend systems.
• Deep expertise in Node.js and JavaScript — the asynchronous model, event loop, and memory behavior — plus solid working proficiency in Python and its production behavior.
• Strong hands-on AWS experience, ideally serverless (Lambda, API Gateway, IAM/STS, VPC, Secrets Manager, CloudWatch) — able to reason about IAM policies, not just apply them.
• Advanced SQL and relational data modeling — set-based query design and a working understanding of why N+1 patterns cause production issues.
• Proven production-debugging ability — root-cause analysis in distributed systems from logs and first principles.
• Strong ownership, sound judgment, and clear written communication — able to make good decisions with incomplete information and explain trade-offs to non-engineers.

Interview Process:

Introductory call-Mutual fit and role overview.

Technical deep-dive- A walkthrough of a challenging production problem you have owned.

Practical exercise -A realistic backend task, or a walkthrough of your own representative code.

System design- Collaborative design on a real scenario.

Final conversation- Values, ownership, compensation, and offer.

KEY RESPONSIBILITIES:

•Build agents with persistent context & memory

•Design self-learning feedback loops

•Implement RAG pipelines for domain knowledge

•Manage conversation state & orchestration

•Integrate with LLM APIs (OpenAI, Claude, open-source)

Iterate fast — ship daily, measure weekly

MUST-HAVE SKILLS

•Python / TypeScript proficiency

•LangChain, CrewAI, AutoGen or custom frameworks

•Experience with vector DBs (Pinecone, Weaviate, Qdrant)

•Prompt engineering & evaluation pipelines

•Understanding of agent architectures (ReAct, tool-use)

Git, CI/CD, containerization basics