Machine Learning Engineer

DocuSign, Inc.; Bengaluru; Hybrid; INR 1,200,000 - 1,800,000; Full time

Job summary

DocuSign, Inc. is looking for a Machine Learning Engineer to join the AI Applications team. This role involves designing and building AI features that enhance user experience, developing scalable systems for autonomous agents, and optimizing model performance.

The ideal candidate will have over 5 years of experience in machine learning, proficiency in Python, and hands-on experience with Kubernetes. The position is hybrid, requiring in-office attendance at least twice a week.

Qualifications

• 5+ years of experience in machine learning engineering or related roles.
• Proficiency in Python and experience with performance optimization.
• Experience with deploying and maintaining ML models in production environments.

Responsibilities

• Build and maintain high-performance distributed systems for agreement processing.
• Design frameworks for multi-agent systems focused on reliability.
• Collaborate with teams to deliver AI capabilities.

Skills

Python design patterns

Asynchronous programming

Performance optimization

Kubernetes (k8s)

Building high-performance distributed systems

Managing data ingestion and model training

Large Language Models (LLMs)

Tools

Azure services

Containerized ML services

Job description

What you’ll do

As a Machine Learning Engineer on the AI Applications team, you will design and build the AI Features that power Docusign’s next generation of intelligent systems and user experience. You will bridge the gap between core AI research and production-grade engineering, developing scalable systems for autonomous agents, advanced retrieval systems, and automated model optimization with the goal of improving the outcomes of our users.

This position is an individual contributor role reporting to the Senior Manager, Machine Learning Engineering.

Responsibility

• Build and maintain high-performance distributed systems to support agreement processing, understanding or creation
• Design frameworks for multi-agent systems, focusing on state management, reliability, and long-running autonomous workflows
• Architect sophisticated Retrieval-Augmented Generation (RAG) pipelines and advanced context management strategies to improve model accuracy and relevance
• Design, implement, and own comprehensive evaluation frameworks, including the construction of domain-specific evaluation sets, golden datasets, and running structured offline/online experiments to maintain a strict quality bar
• Author, version, and optimize production prompts, ensuring high semantic accuracy and robust defenses against prompt injection
• Own and execute end-to-end model fine-tuning processes to enhance feature alignment for tailored agreement use cases
• Implement robust ML pipelines and CI/CD workflows, focusing on observability, telemetry instrumentation, log parsing, and the seamless deployment of generative AI services
• Collaborate cross-functionally with Applied Science and Product Management teams via a joint triage framework to deliver AI capabilities into production-grade features

Job Designation

Hybrid: Employee divides their time between in-office and remote work. Access to an office location is required. (Frequency: Minimum 2 days per week; may vary by team but will be weekly in-office expectation)

Positions at Docusign are assigned a job designation of either In Office, Hybrid or Remote and are specific to the role/job. Preferred job designations are not guaranteed when changing positions within Docusign. Docusign reserves the right to change a position's job designation depending on business needs and as permitted by local law.

What you bring

Basic

• 5+ years of experience in machine learning engineering, software engineering, or related operational roles
• Candidates must demonstrate proficiency in Python design patterns, asynchronous programming, and performance optimization
• Proven experience deploying and managing containerized ML services using Kubernetes (k8s)
• Experience in building high-performance, scalable distributed systems that can support large-scale agreement processing and real-time user experience
• An understanding of the full lifecycle is required, specifically managing data ingestion, model training, and production-grade monitoring
• Direct experience building with Large Language Models (LLMs), specifically implementing complex prompt engineering
• Experience deploying and maintaining ML models in high-traffic, production environments

Preferred

• Cloud Native development experience with Azure services
• Experience designing "agent-loop" architectures that involve tool-use, self-correction, and multi-step reasoning
• Ability to design and own comprehensive evaluation systems, including the creation of "golden datasets" and domain-specific evaluation sets
• Experience with distributed task queues or stateful workflow engines for managing complex, multi-step AI processes

Accommodation: Docusign is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need such an accommodation, or a religious accommodation, during the application process, please contact us at [e-mail].

If you experience any issues, concerns, or technical difficulties during the application process, please get in touch with our Talent organization at [e-mail] for assistance.