Software Engineer - II (AI)

Coderound Ai; Bengaluru; On-site; INR 1,500,000 - 2,400,000; Full time

Job summary

Coderound Ai in Bengaluru seeks an SDE-II AI Engineer to own end-to-end MLOps for computer vision, NLP, and multi-modal models, from training pipelines to production deployment.

You will design scalable pipelines, manage containerized infra with Docker and Kubernetes, implement model monitoring, and collaborate with Research and Backend teams to translate research into reliable services for automated construction take-off and estimation from blueprints.

Responsibilities

• Own the end-to-end MLOps lifecycle, from model packaging and CI/CD to deployment, monitoring, and rollback for computer vision, NLP, and multi-modal models.
• Design and maintain scalable training and inference pipelines for large datasets and models, optimizing for cost, latency, and throughput.
• Build and manage containerized deployment infrastructure (Docker, Kubernetes) for hosted deep learning and geoprocessing services.
• Set up and maintain experiment tracking, model registry, and versioning systems to ensure reproducibility across the research-to-production lifecycle.
• Implement model monitoring and observability drift detection, performance degradation alerts, logging, and dashboards, for models running in production.
• Apply model optimization techniques (quantization, pruning, knowledge distillation) to improve inference efficiency in production.
• Collaborate with Research Engineers, Backend Engineers, and Product teams to translate research ideas into deployable, production-ready services.
• Develop and maintain infrastructure-as-code, monitoring, and logging for all deployed ML/AI software.
• Evaluate, profile, and continuously improve the reliability, scalability, and cost-efficiency of existing ML systems.
• Stay current with evolving MLOps tooling and best practices and evaluate applicability to construction industry challenges.
• As an SDE-II AI Engineer, you will sit at the intersection of our AI Research and Engineering teams, owning the path that takes computer vision, NLP, and multi-modal models from research prototypes to reliable, scalable production systems.
• You will build and operate the infrastructure, pipelines, and tooling that let our models run efficiently in production, powering automated construction take-off and estimation from blueprints, drawings, and PDF documents.
• In this role, you will work closely with Research Engineers and Backend Engineers to close the gap between experimentation and deployment, designing training and inference pipelines, setting up experiment tracking and model versioning, and building the monitoring and observability that keep our AI systems accurate and dependable at scale.

Skills

MLOps

CI/CD

Model monitoring

Experiment tracking

Infra as code

Tools

Docker

Kubernetes

Git

Terraform

Job description

Job Summary

Develops AI-powered construction takeoff, estimating, bid management, and BIM software that automates preconstruction workflows for contractors and field service businesses.

Responsibilities

• Own the end-to-end MLOps lifecycle, from model packaging and CI/CD to deployment, monitoring, and rollback for computer vision, NLP, and multi-modal models.
• Design and maintain scalable training and inference pipelines for large datasets and models, optimizing for cost, latency, and throughput.
• Build and manage containerized deployment infrastructure (Docker, Kubernetes) for hosted deep learning and geoprocessing services.
• Set up and maintain experiment tracking, model registry, and versioning systems to ensure reproducibility across the research-to-production lifecycle.
• Implement model monitoring and observability drift detection, performance degradation alerts, logging, and dashboards, for models running in production.
• Apply model optimization techniques (quantization, pruning, knowledge distillation) to improve inference efficiency in production.
• Collaborate with Research Engineers, Backend Engineers, and Product teams to translate research ideas into deployable, production-ready services.
• Develop and maintain infrastructure-as-code, monitoring, and logging for all deployed ML/AI software.
• Evaluate, profile, and continuously improve the reliability, scalability, and cost-efficiency of existing ML systems.
• Stay current with evolving MLOps tooling and best practices and evaluate applicability to construction industry challenges.
• As an SDE-II AI Engineer, you will sit at the intersection of our AI Research and Engineering teams, owning the path that takes computer vision, NLP, and multi-modal models from research prototypes to reliable, scalable production systems.
• You will build and operate the infrastructure, pipelines, and tooling that let our models run efficiently in production, powering automated construction take-off and estimation from blueprints, drawings, and PDF documents.
• In this role, you will work closely with Research Engineers and Backend Engineers to close the gap between experimentation and deployment, designing training and inference pipelines, setting up experiment tracking and model versioning, and building the monitoring and observability that keep our AI systems accurate and dependable at scale.