Backend & 3D Reconstruction Engineer (Python / ML / AWS)

Trinity Mobile App Lab Pvt. Ltd.; Dadri; On-site; INR 1,500,000 - 2,500,000; Full time

Benefits offered by this job

Ownership of entire pipeline

Spec-driven engineering culture

Real infrastructure in production

Job summary

Trinity Mobile App Lab Pvt. Ltd. is seeking a senior backend engineer to own and operate production-grade Python services for a scalable 3D/ML pipeline on AWS.

You will design async job architectures, REST APIs, and data layers with PostgreSQL and S3, and collaborate with ML/3D teams to deliver robust reconstruction, mesh processing, and object integration. You will work from India (UP) with a strong emphasis on reliability, testing, CI, and cost-aware deployment on cloud infrastructure, while

Qualifications

• 4+ years backend Python; production FastAPI/Flask/Django service ownership.
• Async job architecture: Celery, SQS, or equivalent; idempotency, retry, and dead-letter design.
• REST API design and versioning for mobile clients; job-status reporting patterns.
• Testing discipline: pytest, regression suites, and CI for a pipeline where silent geometric errors are unacceptable.

Responsibilities

• Build the server-side reconstruction and processing pipeline on AWS.
• Own the data layer: PostgreSQL 16 on Amazon RDS (private subnet, KMS encryption), S3 storage, schema design for spatial data.
• Design the async processing pipeline: FastAPI service, job queue, status reporting, retries and failure handling.
• Deploy and operate GPU inference with cost-conscious scaling.

Skills

Backend Python

Async job architecture

REST API design

Testing discipline

Tools

PostgreSQL

AWS

S3

CI/CD

Job description

Trinity Mobile App Lab Pvt. Ltd. is one of the fastest-growing IT and software companies, specializing in building digital products and applications that help organizations expand and innovate. The company develops mobile and web applications with a strong focus on understanding customer needs and delivering personalized solutions. Trinity provides IT services, support, and advanced applications designed to streamline and automate organizational processes. Its vision is to fully automate organizations through tailored applications that drive growth and deliver tangible benefits. Team members have opportunities to learn about Trinity’s latest applications, services, and activities while contributing to impactful digital solutions.

About the Product

Soul Home captures a user’s home in 3D through three tiers: LiDAR (iOS), ARCore depth (Android), and a photo/panorama fallback for phones with neither. The mobile team owns capture; you own everything that happens after upload — reconstruction, mesh processing, floor-plan extraction, object placement, and the processing pipeline that turns raw scans into structured spatial data for a deterministic Vastu/Feng Shui rule engine.

The Role

You will build the server-side reconstruction and processing pipeline on AWS. The centerpiece is the fallback tier: reconstructing room geometry from guided photos or panoramas using monocular depth and layout-estimation models (HorizonNet / LGT-Net class networks, already selected), so that users without AR-capable phones still get a usable spatial model. You will also own mesh cleanup and normalization for all three tiers, the async job pipeline, and GPU inference deployment.

Core Responsibilities

• Build the photo-to-layout reconstruction pipeline: panorama preprocessing, equirectangular handling, layout estimation (HorizonNet/LGT-Net), monocular depth (Depth Anything v2 / MiDaS), and conversion of network output into wall/opening geometry
• Build the mesh processing stage for all tiers using Open3D/trimesh: cleanup, decimation, plane fitting, floor-plan extraction, unit and coordinate normalization
• Place YOLO v11 object-detection outputs (from a 76-object catalog) into room coordinates within the reconstructed model
• Design the async processing pipeline: FastAPI service, job queue (Celery/SQS), status reporting to mobile clients, retry and failure handling
• Deploy and operate GPU inference (ONNX Runtime / TorchServe / PyTorch on EC2 GPU instances) with cost-conscious scaling
• Own the data layer: PostgreSQL 16 on Amazon RDS (private subnet, KMS encryption), S3 scan-artifact storage, schema design for spatial data (PostGIS a plus)
• Define and version the spatial data contract consumed by the downstream rule engine
• Classical photogrammetry fallback (COLMAP / OpenMVG+OpenMVS) where photo sets permit, with honest handling of indoor failure cases (textureless walls, low light)

Must-Have Skills

• 4+ years backend Python; production FastAPI/Flask/Django service ownership
• Async job architecture: Celery, SQS, or equivalent; idempotency, retry, and dead-letter design
• REST API design and versioning for mobile clients; job-status reporting patterns
• Testing discipline: pytest, regression suites, and CI for a pipeline where silent geometric errors are unacceptable

ML Inference (production, not research)

• Hands-on PyTorch inference in production: model loading, batching, ONNX export, GPU memory management
• Deploying and operating models behind an API (ONNX Runtime, TorchServe, or plain PyTorch serving)
• Understanding of inference cost/latency trade-offs and GPU instance right-sizing

3D & Image Processing

• Practical 3D geometry processing: Open3D or trimesh — point clouds vs meshes, plane fitting, decimation, coordinate transforms and unit normalization
• Ability to turn model output (layout edges, depth maps) into clean wall/opening vector geometry

Data & AWS

• PostgreSQL 16 schema design and disciplined migrations (Alembic or equivalent)
• S3 lifecycle and artifact-versioning design for large binary scan data
• Clear written communication; comfortable working from and contributing to formal specs

Good-to-Have Skills

• Direct experience with HorizonNet, LGT-Net, or other room-layout estimation networks
• COLMAP or OpenMVG/OpenMVS photogrammetry pipelines and their indoor failure modes
• PostGIS or other spatial-query experience (zone/direction queries on room geometry)
• YOLO family deployment and back-projection of 2D detections into 3D room coordinates
• glTF/GLB/USDZ/PLY format handling and conversion tooling
• Docker and infrastructure-as-code (Terraform/CDK) for reproducible pipeline environments
• Observability for ML pipelines: structured logging, metrics, per-stage failure dashboards
• Batch GPU scheduling / spot-instance cost optimization
• Prior work with floor plans, BIM/CAD data, real-estate, or interior-mapping products
• Familiarity with panorama stitching and 360° capture pipelines

What We Offer

• Ownership of an entire pipeline from scan upload to structured spatial model, working directly with the founder
• Spec-driven engineering culture with formal architecture documents, rule catalogs, and a deterministic-core / no-guessing engineering philosophy
• An AWS environment already in production (RDS PostgreSQL migration underway) — you build on real infrastructure, not slides