Shared jobSoftware Engineer - II (AI)
Noida · Mid Level
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Job description
- 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.
- 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).
- Experience with cloud environments (GCP, AWS, or Azure) and infrastructure-as-code practices.
- Familiarity with messaging and data processing pipelines (e.g., Apache Kafka, RabbitMQ) and distributed web services.
- Understanding of model optimization techniques such as quantization, pruning, and knowledge distillation - Good to have.
- Experience with version control systems (e.g., Git) and project tracking tools (e.g., JIRA).
- Familiarity with PostGIS or other geo-databases and Geographic Information Systems - Good to have.
- Strong analytical and problem-solving skills with a passion for building reliable, scalable systems; comfortable working in a fast-paced, agile, startup-like environment.
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