Senior Staff Machine Learning Engineer
Bengaluru, Karnataka · Bengaluru · 5+ years experience · Posted Sep 30, 2026
At a glance
- Experience
- 5+ years experience
- Location
- Bengaluru, Karnataka · Bengaluru
About the role
- Netradyne is seeking a Senior Staff Machine Learning Engineer to set technical direction for a cross-functional team of Data Scientists and Data/SW/ML Engineers.
- The role involves owning the architecture of large-scale cloud ML systems end-to-end, including data ingestion, feature pipelines, training infrastructure, model serving, monitoring, and retraining.
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.
- Experience with observability and production operations — metrics, tracing, logging, alerting and incident response for services and ML pipelines.
Preferred qualifications
- 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.
Skills
- Python
- Java
- Rust
- C++
- AWS
- SQL
- Terraform
- Kubernetes
- Helm
- Kafka
- FastAPI
- MLFlow
Show all 15 skills
- LangGraph
- Tableau
- Grafana
