About this opportunity
DocuSign, Inc. lists this Senior Software Engineer - AI opportunity in san francisco, California. Review the employer’s description below for duties, qualifications and application requirements.
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
Our Digital Technology Services team is in the business of trust and reliability. We create, maintain and operate scalable technology and data solutions that deliver an exceptional experience for our internal & external customers. We embrace Agile principles and values, favor DevOps practices, and view infrastructure as code, all while we create an SW infrastructure that scales and supports our growth and ambitious vision. This requires a smart, highly collaborative team who can identify, investigate, and implement new technologies to continue securely scaling our global business. We are seeking a Senior Software Engineer to design, build, and scale intelligent, enterprise-grade software solutions, with a strong focus on agentic Al systems and complex integrations across Application Engineering platforms. This role is a hands‑on engineering position requiring deep expertise in software design, distributed systems, APIs, and Al-enabled architectures, rather than functional system configuration. The Senior Software Engineer will develop and operate custom‑built applications, Al agents, middleware services, and integration frameworks that connect core technology platforms with enterprise and external systems. A key aspect of this role is the application of agentic Al patterns, including orchestration, tool‑using agents, Retrieval-Augmented Generation (RAG), and workflow automation, to improve system intelligence, resilience, and developer productivity. This role partners closely with Product Management, Enterprise Architecture, and technology stakeholders but remains fundamentally an engineering role, accountable for code quality, system reliability, scalability, and security.
This position is an individual contributor role reporting to the Director, Application Support and Operations.
Responsibility
Conduct applied Al research to translate theoretical GenAl advancements into production-ready software features
Lead Technical Feasibility Studies and rapid prototyping to provide the engineering foundation for "build vs. buy" architectural decisions
Engineer Production-Grade NLP algorithms and information retrieval systems using SpaCy, NLTK, and Hugging Face to drive core product capabilities
Design, build, and maintain scalable RAG architectures that connect foundational Large Language Models (LLMs) to proprietary enterprise databases
Evaluate and apply appropriate embedding models, vector databases, and LLMs based on cost, latency, security, and performance requirements
Build enterprise-grade conversational interfaces and analytical Al tools (QueryGPT) that interface directly with structured data systems via custom middleware
Design and Build autonomous multi-agent frameworks (e.g., CrewAl, LangGraph) and scalable agentic platforms, focusing on distributed system architecture and secure execution environments
Develop Custom Extensions and API-based integrations for LLM models, creating sophisticated Al assistants through backend systems programming
Execute Model Engineering through supervised fine-tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) to optimize model weight distribution for scalability and reliability
Develop algorithmic prompt-chaining logic and maintain a centralized, version-controlled prompt library integrated into the CI/CD pipeline
Architect and Develop end-to-end evaluation pipelines for LLMs/SLMs, Engineering complex telemetry to capture performance, quantization efficiency, and fine-tuning convergence metrics
Own the technical documentation, code maintainability, and reproducibility of the Al infrastructure, ensuring alignment with engineering excellence standards
Bridge time zones effectively; ensure crisp handoffs and decision velocity with US counterparts
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
Bachelor's or Master's degree in Computer Science or a related field
6+ years of relevant experience with a Master's degree, or 8+ years with a Bachelor's degree
Experience developing and deploying GenAl-powered applications such as intelligent chatbots, Al copilots, and autonomous agents
Experience with Large Language Models (LLMs), transformer architectures (e.g., BERT, GPT, T5), and their applications in text generation, summarization, question answering, and code synthesis
Experience with Retrieval-Augmented Generation (RAG), embedding techniques, knowledge graphs, and fine-tuning/training of large language models (LLMs)
Experience in natural language processing (NLP), prompt engineering, instruction tuning, context window optimization, advanced tokenization strategies, and leveraging pre-trained LLMs (via APIs or open-source models)
Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, and agentic/multi-agent orchestration tools like LangGraph, CrewAl, or similar
Experience developing and implementing an interactive search platform Glean
Experience with programming languages such as Python and Bash, as well as frameworks/tools like React and Streamlit
Experience with any copilot tools for coding such as Github copilot or Cursor
Experience with vector databases such as FAISS, Pinecone, Weaviate, and Chroma for embedding storage and retrieval
Experience with data preprocessing, augmentation, and visualization techniques
Experience contributing to GenAl projects from ideation through deployment, iteration, and evaluation of LLM performance
Experience working with containerization and orchestration technologies like Docker, Kubernetes, and AWS ECS
Experience with key AWS services including VPC, IAM, MWAA (Managed Workflows for Apache Airflow), and ECS
Experience with software development best practices including Git, testing, CI/CD pipelines, infrastructure as code (Terraform), automation, and MLOps for GenAl
Preferred
Strong commitment to engineering excellence through automation,
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Worksite address
san francisco, CA, 94199, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.