CodeRound AI
Website:
coderound.io
Company:
https://www.linkedin.com/company/coderoundai
Seniority: Mid-Senior level
Industries: Technology, Information and Media
Job details:
๐๐ฏ๐ผ๐๐ ๐๐ต๐ฒ ๐ท๐ผ๐ฏ
This role is for one of our client companies โ a VC-backed Construction Technology (ConTech) startup that has raised $49.7M USD in funding.
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐: Up to 40LPA
Apply once and, if selected, get access to up to 20 remote and onsite interview opportunities.
๐ ๐ช๐ต๐ฎ๐ ๐ช๐ฒ'๐ฟ๐ฒ ๐๐๐ถ๐น๐ฑ๐ถ๐ป๐ด
CodeRound AI matches the top 5% tech talent with the fastest-growing, VC-funded AI startups across Silicon Valley and India.
Top-tier product startups across the US, UK, EU, UAE, and India have hired top engineers through CodeRound.
๐ ๐ฆ๐ผ๐ณ๐๐๐ฎ๐ฟ๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ - ๐๐ (๐๐) (3+ ๐ฌ๐ฒ๐ฎ๐ฟ๐ ๐ผ๐ณ ๐๐
๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ)
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.
๐งฉ ๐ช๐ต๐ฎ๐ ๐ฌ๐ผ๐'๐น๐น ๐๐ผ
- 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 โ 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.
โ
๐ฌ๐ผ๐'๐ฟ๐ฒ ๐ฎ ๐๐ฟ๐ฒ๐ฎ๐ ๐๐ถ๐ ๐๐ณ ๐ฌ๐ผ๐
- 3+ years of experience in MLOps, ML infrastructure, or applied AI/ML engineering, with exposure to Computer Vision or NLP systems.
- Hands-on experience with workflow orchestration frameworks (preferably Temporal) for building reliable, fault-tolerant, long-running distributed workflows.
- Strong proficiency in Python and hands-on experience with ML frameworks such as PyTorch, TensorFlow, OpenCV, or HuggingFace Transformers.
- Hands-on experience with Docker, Kubernetes, and containerized ML deployment pipelines in production environments.
- Experience building and maintaining CI/CD pipelines for ML systems (e.g., Jenkins, GitHub Actions, GitLab CI).
- Working knowledge of experiment tracking and model registry tools (e.g., MLflow, Weights & Biases, DVC).
โจ ๐ช๐ต๐ ๐๐ผ๐ถ๐ป ๐จ๐?
- Own meaningful product decisions from day one at a funded startup
- Work with a sharp team shipping fast in a high-growth environment
- Accelerate your career with outsized responsibility and visibility
- Build something that scales โ not slide decks
Click on Apply to know more.
