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 We are seeking a Software Engineer to design, build, and scale intelligent, enterprise-grade software solutions, with a strong focus on agentic AI systems and complex integrations across Legal Technology and Docusign platforms. This role is a hands-on engineering position requiring deep expertise in software design, distributed systems, APIs, and AI-enabled architectures. The Software Engineer will develop and operate custom-built applications, AI agents, middleware services, and integration frameworks that connect core Legal Technology and Docusign platforms with enterprise and external systems. A key aspect of this role is the application of agentic AI patterns, including orchestration, tool-using agents, Retrieval-Augmented Generation (RAG), and workflow automation. This role partners closely with Product Management, Enterprise Architecture, and Legal Technology stakeholders but remains fundamentally an engineering role, accountable for code quality, system reliability, scalability, and security. This position is n individual contributor role reporting to the Senior Director, Application Engineering. Responsibility Conduct applied AI research to translate theoretical GenAI 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 AI tools (QueryGPT) that interface directly with structured data systems via custom middleware Design and Build autonomous multi-agent frameworks (e.g., CrewAI, 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 AI 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 AI infrastructure, ensuring alignment with engineering excellence standards 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 10+ years of relevant experience with a Master’s degree, or 12+ years with a Bachelor’s degree Proven experience in developing and deploying GenAI-powered applications such as intelligent chatbots, AI copilots, and autonomous agents Strong understanding of Large Language Models (LLMs), transformer architectures (e.g., BERT, GPT, T5), and their applications in text generation, summarization, question answering, and code synthesis Strong understanding of 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) Proficiency with LLM orchestration frameworks such as LangChain, LlamaIndex, and agentic/multi-agent orchestration tools like LangGraph, CrewAI, or similar Direct working experience in developing and implementing an interactive search platform Glean Proficiency in 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 Hands-on experience with vector databases such as FAISS, Pinecone, Weaviate, and Chroma for embedding storage and retrieval Familiarity with data preprocessing, augmentation, and visualization techniques Proven track record of contributing to GenAI projects from ideation through deployment, iteration, and evaluation of LLM performance Experience working with containerization and orchestration technologies like Docker, Kubernetes, and AWS ECS Hands-on expertise with key AWS services including VPC, IAM, MWAA (Managed Workflows for Apache Airflow), and ECS Familiarity with software development best practices including Git, testing, CI/CD pipelines, infrastructure as code (Terraform), automation, and MLOps for GenAI Preferred Strong commitment to engineering excellence through automation, innovation, and documentation One or more certifications such as Cloud, Solution Architect, Technical Architect, or GenAI-related certifications Proficiency in cloud platforms such as AWS and Azure Strong problem-solving skills and the ability to think creatively Strong collaboration skills in cross-functional teams (Product, Design, ML, Data Engineering) Ability to explain complex GenAI concepts to both technical and non-technical stakeholders Life at Docusign Working here 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 accommodations@docusign.com. If you experience any issues, concerns, or technical difficulties during the application process please get in touch with our Talent organization at taops@docusign.com for assistance. Applicant and Candidate Privacy Notice #LI-Hybrid #LI-SA4
Software Engineer - AI
DocuSign · Enterprise · Bangalore, India · IT Infrastructure & Operations
Software Engineer to design, build, and scale intelligent, enterprise-grade software solutions, with a strong focus on agentic AI systems and complex integrations. This role involves developing custom applications, AI agents, middleware services, and integration frameworks, applying agentic AI patterns like orchestration, tool-using agents, RAG, and workflow automation. Responsibilities include applied AI research, engineering NLP algorithms, designing RAG architectures, building conversational interfaces, developing multi-agent frameworks, executing model engineering (SFT/RLHF), prompt engineering, and architecting evaluation pipelines.
What you'd actually do
- Design, build, and scale intelligent, enterprise-grade software solutions, with a strong focus on agentic AI systems and complex integrations across Legal Technology and Docusign platforms.
- The Software Engineer will develop and operate custom-built applications, AI agents, middleware services, and integration frameworks that connect core Legal Technology and Docusign platforms with enterprise and external systems.
- A key aspect of this role is the application of agentic AI patterns, including orchestration, tool-using agents, Retrieval-Augmented Generation (RAG), and workflow automation.
- Responsibility Conduct applied AI research to translate theoretical GenAI advancements into production-ready software features
- Engineer Production-Grade NLP algorithms and information retrieval systems using SpaCy, NLTK, and Hugging Face to drive core product capabilities
Skills
Required
- Software design
- Distributed systems
- APIs
- AI-enabled architectures
- NLP algorithms
- Information retrieval systems
- RAG architectures
- Embedding models
- Vector databases
- LLMs
- Conversational interfaces
- Analytical AI tools
- Multi-agent frameworks
- Agentic platforms
- Distributed system architecture
- Secure execution environments
- LLM model extensions
- API-based integrations
- AI assistants
- Backend systems programming
- Model Engineering (SFT/RLHF)
- Prompt-chaining logic
- Prompt library management
- CI/CD pipeline integration
- Evaluation pipelines for LLMs/SLMs
- Telemetry engineering
- Technical documentation
- Code maintainability
- Reproducibility of AI infrastructure
- Python
- Bash
- React
- Streamlit
- Docker
- Kubernetes
Nice to have
- SpaCy
- NLTK
- Hugging Face
- LangChain
- LlamaIndex
- LangGraph
- CrewAI
- FAISS
- Pinecone
- Weaviate
- Chroma
- Github copilot
- Cursor
What the JD emphasized
- strong focus on agentic AI systems
- deep expertise in software design, distributed systems, APIs, and AI-enabled architectures
- application of agentic AI patterns
- applied AI research
- production-ready software features
- Engineer Production-Grade NLP algorithms
- scalable RAG architectures
- embedding models, vector databases, and LLMs
- enterprise-grade conversational interfaces
- autonomous multi-agent frameworks
- scalable agentic platforms
- distributed system architecture
- secure execution environments
- sophisticated AI assistants
- supervised fine-tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF)
- optimize model weight distribution for scalability and reliability
- algorithmic prompt-chaining logic
- centralized, version-controlled prompt library
- end-to-end evaluation pipelines for LLMs/SLMs
- Engineering complex telemetry
- technical documentation, code maintainability, and reproducibility of the AI infrastructure
- Proven experience in developing and deploying GenAI-powered applications
- Strong understanding of Large Language Models (LLMs), transformer architectures
- Strong understanding of 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
- Proficiency with LLM orchestration frameworks
- agentic/multi-agent orchestration tools
- Direct working experience in developing and implementing an interactive search platform
- Hands-on experience with vector databases
- embedding storage and retrieval
- Proven track record of contributing to GenAI projects from ideation through deployment, iteration, and evaluation of LLM performance
- Experience working with containerization and orchestration technologies like Docker, Kubernete
Other signals
- design and build scalable RAG architectures
- design and build autonomous multi-agent frameworks
- develop algorithmic prompt-chaining logic
- architect and develop end-to-end evaluation pipelines for LLMs/SLMs