Senior Staff Machine Learning Engineer

Engg; Bengaluru; On-site; INR 4,000,000 - 7,500,000; Full time

Job summary

Netradyne is seeking an experienced ML architecture leader in Bengaluru to own end-to-end cloud ML systems, from data ingestion to model serving. You will design scalable pipelines, apply Gen-AI techniques, and ensure reliable operations with strong observability and cost controls.

The role requires deep expertise in distributed systems, Python, cloud platforms, and MLOps, with a track record of delivering production ML at scale.

Qualifications

• Advanced degree or higher in a relevant field with strong ML background.
• Proven experience building and shipping production ML systems at scale.
• Strong software engineering fundamentals and cloud architecture knowledge.

Responsibilities

• Own architecture of large-scale cloud ML systems end to end including data pipelines and training infra.
• Design and deploy production-ready scalable cloud solutions using Gen-AI and traditional ML models.
• Architect distributed, fault-tolerant services with observability and cost controls.
• Apply ML/DL techniques to uncover patterns in large datasets and automate workflows.
• Lead standardization across teams for CI/CD, testing, and releases; mentor senior engineers.
• Collaborate with cross-functional teams to implement data-driven business solutions.

Skills

Python

Distributed systems

MLOps

Data pipelines

Gen-AI

Education

PhD in Data Science or related

M.Tech in CS/EE

Tools

AWS

Kubernetes

Terraform

CI/CD

Job description

Netradyne harnesses the power of Computer Vision and Edge Computing to revolutionize the modern-day transportation ecosystem. We are a leader in fleet safety solutions. With growth exceeding 4x year over year, our solution is quickly being recognized as a significant disruptive technology. Our team is growing, and we need forward-thinking, uncompromising, competitive team members to continue to facilitate our growth.

Job Responsibilities

• Owning the architecture of large-scale cloud ML systems end to end — data ingestion and feature pipelines, training infrastructure, model serving, monitoring and retraining.
• Design, develop and deploy production ready scalable cloud solutions that utilize Gen-AI, agentic AI, DNN, Traditional ML models, data-driven rules and ETL pipelines.
• Architecting distributed, fault-tolerant services and data platforms that operate reliably at high throughput, with clear SLAs, observability and cost controls.
• Applying advanced statistical methods, machine learning and deep learning techniques to uncover trends, patterns, and anomalies in large-scale datasets.
• Creating robust frameworks and tools to automate and enhance data mining, labeling, model training, and validation processes for internal ML/DL initiatives.
• Setting engineering standards across teams — design review, testing strategy, CI/CD and release practice — and mentoring Staff and Senior engineers.
• Collaborating closely with cross-functional teams to identify and implement data-driven solutions addressing key business challenges.
• Conducting studies, setting up automation tools and frameworks, and regularly publishing internal and external KPI audits.
• Develop and maintain ROI models and frameworks to quantify the business impact of data science initiatives.

Requirements

• B. Tech, M. Tech or PhD in Data Science, Computer Science, Electrical Engineering, Operations Research, Statistics, Mathematics or a related area.
• At least 8 years of working experience in machine learning, data science or a related domain, including 5+ years building and shipping production ML systems at scale.
• Demonstrated depth on both sides of the role: building distributed data and ETL pipelines, and training, tuning and deploying models in production.
• Strong large-scale software engineering fundamentals: distributed systems, concurrency, microservice and API design, caching, queueing, idempotency and failure handling.
• Proven experience designing and operating systems on public cloud at scale — AWS preferred (Kinesis, SQS, EKS, Lambda, Auto Scaling Groups, S3), including cost, capacity and reliability trade-offs.
• Strong foundational knowledge in Statistics, Probability Theory, Machine Learning and Gen-AI.
• Excellent programming skills – Python (required) and Java/Rust/C++ (desired), with strong fundamentals in object-oriented programming, algorithms, and data structures.
• Good understanding of database internals and schema design for relational (RDBMS) and non-relational (NoSQL) data stores, including the ability to write and reason about complex SQL.
• Experience with transformer architectures and large language models (LLMs), and with Gen-AI tools and workflows.
• Knowledge of best practices in software development, including version control, code review, automated testing, continuous integration and continuous delivery.
• Experience with observability and production operations — metrics, tracing, logging, alerting and incident response for services and ML pipelines.
• Proven ability to influence technical decisions beyond one’s own team.

Desired Skills

• Agentic AI systems — tool use, planning, multi-agent orchestration, memory, guardrails and agent evaluation.
• Hands-on experience with the Claude Agent SDK, OpenAI Agents SDK and Model Context Protocol (MCP) servers and connectors.
• AI-native development practice — working effectively with coding agents, GitHub Copilot, Claude Code or similar, and setting team conventions for their use.
• Test-Driven Development (TDD) and Spec-Driven Development (SDD); designing specs and evals that agents and humans can both work against.
• LLMOps: prompt and context management, retrieval-augmented generation, model routing, caching, token cost optimisation and offline/online eval harnesses.
• Infrastructure as code and container orchestration — Terraform, Kubernetes, Helm; multi-region and blue-green or canary deployment patterns.
• Streaming and service technologies such as Kafka streams, Queues, Rest API and gRPC systems.
• Tools: FastAPI, MLFlow, Huggingface pipelines, LangGraph, OpenAI, Anthropic API.
• Experience with MLOps tools and practices for continuous deployment and monitoring of AI models.
• Experience with data visualization tools like Tableau, Grafana, Plotly-Dash.

Netradyne is an equal-opportunity employer.

We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status, or any legally protected status.

Applicants only - Recruiting agencies do not contact.

Netradyne is an equal-opportunity employer.