8 hours ago
Bengaluru, IndiaStaff+
Responsibilities
- Set technical direction for cross-functional teams of Data Scientists and Data, Software, and ML Engineers.
- Own the architecture of large-scale cloud ML systems covering data ingestion, feature pipelines, training infrastructure, model serving, monitoring, and retraining.
- Design, develop, and deploy scalable cloud solutions using Gen-AI, agentic AI, DNN, traditional ML models, data-driven rules, and ETL pipelines.
- Architect distributed, fault-tolerant services and data platforms with high throughput, observability, SLAs, and cost controls.
- Apply statistical, machine learning, and deep learning techniques to identify trends, patterns, and anomalies in large-scale datasets.
- Create frameworks and tools for data mining, labeling, model training, and validation.
- Set engineering standards for design reviews, testing, CI/CD, and release practices, and mentor Staff and Senior Engineers.
- Collaborate with cross-functional teams on data-driven business solutions and publish internal and external KPI audits.
- Develop ROI models and frameworks to measure the business impact of data science initiatives.
Requirements
- Bachelor's, master's, or PhD-level education in Data Science, Computer Science, Electrical Engineering, Operations Research, Statistics, Mathematics, or a related field.
- At least 8 years of experience in machine learning, data science, or a related domain, including 5 or more years building and shipping production ML systems at scale.
- Demonstrated experience building distributed data and ETL pipelines and training, tuning, and deploying production models.
- Strong software engineering fundamentals in distributed systems, concurrency, microservice and API design, caching, queueing, idempotency, and failure handling.
- Experience designing and operating public-cloud systems at scale, preferably on AWS, with cost, capacity, and reliability trade-off analysis.
- Strong knowledge of statistics, probability theory, machine learning, and generative AI.
- Required proficiency in Python; Java, Rust, or C++ experience is desired.
- Knowledge of object-oriented programming, algorithms, data structures, database internals, relational and non-relational data stores, schema design, and complex SQL.
- Experience with transformer architectures, large language models, generative AI tools, and AI workflows.
- Knowledge of version control, code review, automated testing, continuous integration, and continuous delivery.
- Experience with observability and production operations, including metrics, tracing, logging, alerting, and incident response.
- Ability to influence technical decisions beyond the immediate team.
- Desired experience includes agentic AI systems, Claude Agent SDK, OpenAI Agents SDK, Model Context Protocol, coding agents, GitHub Copilot, Claude Code, LLMOps, Terraform, Kubernetes, Helm, Kafka, FastAPI, MLflow, Hugging Face pipelines, LangGraph, OpenAI, Anthropic API, MLOps, Tableau, Grafana, or Plotly-Dash.
Benefits
- The company is committed to an inclusive and diverse workplace and is an equal-opportunity employer.
Tech Stack
Categories
About Netradyne
Netradyne builds an AI vision and video telematics platform for commercial vehicle fleets, using edge cameras and cloud analytics to improve safety, compliance, and operations. It sells hardware-enabled SaaS that analyzes driving in real time and delivers coaching and incident insights to managers and drivers. Founded in 2015 and headquartered in San Diego, it is privately held and serves fleet customers across North America, Europe, and Asia.