Machine Learning Engineer

Docusign; Bengaluru; Hybrid; INR 1,500,000 - 2,500,000; Full time

Job summary

Docusign is seeking a Machine Learning Engineer to join their AI Applications team in Bengaluru, Karnataka. This role focuses on designing and building AI features and requires strong experience in machine learning engineering and software development.

The successful candidate will develop scalable systems and collaborate with cross-functional teams to enhance Docusign's intelligent systems. A hybrid work model is offered, allowing for flexibility between remote work and in-office collaboration.

Qualifications

• 5+ years of experience in machine learning engineering or related roles.
• Proficiency in Python design patterns and asynchronous programming.
• Experience with containerized ML services using Kubernetes.

Responsibilities

• Build high-performance distributed systems for agreement processing.
• Design frameworks for multi-agent systems focusing on reliability.
• Architect advanced context management strategies to improve model accuracy.

Skills

Machine learning engineering

Software engineering

Python design patterns

Asynchronous programming

Kubernetes

Large Language Models

Job description

Company Overview

Docusign brings agreements to life. Over 1.5 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, Docusign unleashes business‑critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity. Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e‑signature and contract lifecycle management (CLM).

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.

Responsibilities

• 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)

What you bring

Basic Qualifications

• 5+ years of experience in machine learning engineering, software engineering, or related operational roles
• Proficiency in Python design patterns, asynchronous programming, and performance optimization
• Experience deploying and managing containerized ML services using Kubernetes (k8s)
• Experience building high‑performance, scalable distributed systems that can support large‑scale agreement processing and real‑time user experience
• Understanding of the full lifecycle, 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 Qualifications

• 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

Life at Docusign

Docusign is committed to building trust and making the world more agreeable for our employees, customers and the communities in which we live and work. You can count on us to listen, be honest, and try our best to do what’s right, every day. At Docusign, everything is equal. We each have a responsibility to ensure every team member has an equal opportunity to succeed, to be heard, to exchange ideas openly, to build lasting relationships, and to do the work of their life. Best of all, you will be able to feel deep pride in the work you do, because your contribution helps us make the world better than we found it. And for that, you’ll be loved by us, our customers, and the world in which we live.

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].