Senior Staff Machine Learning Engineer

Job Responsibilities:

As a Senior Staff Machine Learning Engineer, you will set technical direction to our cross-functional team consisting of Data Scientists and Data/SW/ML Engineers. Your primary responsibilities will include:

• 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.