GenAI Engineer

Top Gen AI Jobs; Bengaluru; Hybrid; INR 3,694,000 - 4,957,000; Full time

Benefits offered by this job

Hybrid work arrangement

Office access in Bengaluru

Job summary

Docusign is seeking a GenAI Engineer to design, develop, and deploy AI applications for enterprise use cases. The role focuses on AI platform work, LLM infrastructure, and agentic workflows within a dynamic team.

The candidate will contribute to LLM gateway design, observability, and multi-provider integration while collaborating with data science, product, and engineering teams to drive AI initiatives.

Qualifications

• 5+ years of professional experience in software engineering, platform engineering, DevOps, or AI/ML infrastructure.
• Hands-on experience with LLM APIs and production use of large language models.
• Familiarity with LLM orchestration and gateway tools such as LiteLLM, LangChain, or similar frameworks.
• Experience with enterprise search or low-code / no-code AI platforms like Glean or Gemini Enterprise Apps.
• Experience building or operating AI agents or agentic workflows using frameworks like LangGraph, CrewAI, or custom implementations.
• Working knowledge of evaluation approaches for LLM applications.
• Proficiency in Python
• Experience with AWS cloud services and infrastructure-as-code tools such as Terraform
• Experience building CI/CD pipelines using tools like GitHub Actions, Azure DevOps, or Jenkins

Responsibilities

• Design, develop, and deploy generative AI applications for complex enterprise problems.
• Contribute to the design, development, and operations of the AI platform.
• Work on LLM infrastructure, agent systems, and AI platforms such as Glean, Gemini Enterprise Apps, or Claude Cowork.
• Support the Glean or other AI platform through sharing, agent development, enablement, and vendor collaboration.
• Build and maintain the LLM gateway with multi-provider routing, fallback configuration, caching, cost tracking, FinOps, and guardrail integration.
• Develop and maintain LLM observability capabilities including prompt/response logging, token and cost attribution, latency tracking, failure mode clustering, hallucination detection, and input drift monitoring.
• Build, test, and iterate on AI agents and agentic workflows with multi-step tool use, orchestration, error handling, and human-in-the-loop mechanisms.
• Integrate and manage MCP servers to connect agents and LLM applications with external tools and data sources.
• Design and execute evaluation frameworks for LLM applications and agents.
• Support VectorDB infrastructure including ingestion pipelines, chunking strategies, retrieval quality measurement, and integration with the AI platform.
• Maintain infrastructure-as-code, CI/CD pipelines, and cloud resources underpinning the AI platform.
• Collaborate with Data Science, Product, and Engineering teams to resolve platform issues and improve developer experience.

Skills

LLM

RAG

LangChain

Vector Database

Gen AI

LLM APIs

LiteLLM

Gemini Enterprise Apps

LLM Security

LLM Observability

Python

Glean

LangGraph

CrewAI

AWS

Tools

Terraform

GitHub Actions

Azure DevOps

Jenkins

Job description

GenAI Engineer

Company: Docusign

Location: Hybrid, Bengaluru

Experience: 5+ years

Salary: $38.6K–51.8K/yr

Contract: Full-time

Docusign is seeking a Generative AI Engineer to help design, develop, and deploy AI applications for enterprise use cases. The role focuses on AI platform work, LLM infrastructure, and agentic workflows in a dynamic team.

Skills Required

• LLM
• RAG
• LangChain
• Vector Database
• Gen AI
• LLM APIs
• LiteLLM
• Gemini Enterprise Apps
• LLM Security
• LLM Observability
• Python
• Glean
• LangGraph
• CrewAI
• AWS

Experience

• 5+ years of professional experience in software engineering, platform engineering, DevOps, or AI/ML infrastructure
• Hands-on experience with LLM APIs and production use of large language models
• Familiarity with LLM orchestration and gateway tools such as LiteLLM, LangChain, or similar frameworks
• Experience with enterprise search or low-code / no-code AI platforms like Glean or Gemini Enterprise Apps
• Experience building or operating AI agents or agentic workflows using frameworks like LangGraph, CrewAI, or custom implementations
• Working knowledge of evaluation approaches for LLM applications
• Proficiency in Python
• Experience with AWS cloud services and infrastructure-as-code tools such as Terraform
• Experience building CI/CD pipelines using tools like GitHub Actions, Azure DevOps, or Jenkins

Responsibilities

• Design, develop, and deploy generative AI applications for complex enterprise problems
• Contribute to the design, development, and operations of the AI platform
• Work on LLM infrastructure, agent systems, and AI platforms such as Glean, Gemini Enterprise Apps, or Claude Cowork
• Support the Glean or other AI platform through sharing, agent development, enablement, and vendor collaboration
• Build and maintain the LLM gateway with multi-provider routing, fallback configuration, caching, cost tracking, FinOps, and guardrail integration
• Develop and maintain LLM observability capabilities including prompt/response logging, token and cost attribution, latency tracking, failure mode clustering, hallucination detection, and input drift monitoring
• Build, test, and iterate on AI agents and agentic workflows with multi-step tool use, orchestration, error handling, and human-in-the-loop mechanisms
• Integrate and manage MCP servers to connect agents and LLM applications with external tools and data sources
• Design and execute evaluation frameworks for LLM applications and agents
• Support VectorDB infrastructure including ingestion pipelines, chunking strategies, retrieval quality measurement, and integration with the AI platform
• Maintain infrastructure-as-code, CI/CD pipelines, and cloud resources underpinning the AI platform
• Collaborate with Data Science, Product, and Engineering teams to resolve platform issues and improve developer experience

Additional Responsibilities

• Work as an individual contributor reporting to the Senior Director, Data Platform and ML Platform
• Adapt quickly to changing requirements and ambiguity
• Help improve employee productivity, developer productivity, and business growth through AI initiatives

Nice to Have

• Experience with Model Context Protocol by building, integrating, or consuming MCP servers
• Hands-on experience with LLM observability tooling such as Arize, Braintrust, Datadog LLM monitoring, LangSmith, or custom tracing solutions
• Familiarity with RAG architectures
• Exposure to prompt injection testing, LLM security, and guardrail implementation
• Experience managing a search platform such as Glean or Gemini Enterprise Apps
• Familiarity with real-time inference architectures including serverless patterns with AWS Lambda
• Understanding of semantic caching, intelligent model routing, or FinOps for LLM cost optimization
• Background in a SaaS or enterprise software environment
• Strong troubleshooting skills across LLM application layers
• Solid communication skills and ability to work cross-functionally with data scientists, product managers, and engineers

Other

• Company overview: Docusign serves over 1.5 million customers and more than a billion people in over 180 countries
• The company focuses on intelligent agreement management and e-signature / contract lifecycle management
• Hybrid work arrangement with minimum 2 days per week in-office; access to an office location is required
• Docusign assigns roles as In Office, Hybrid, or Remote based on business needs and local law
• Commitment to building trust, equal opportunity, openness, and inclusion
• Reasonable accommodations are available for qualified individuals with disabilities during the job application process

More Skills

prompt design, function calling, tool calling, streaming, structured outputs, agent systems, Terraform, GitHub Actions, Azure DevOps, Jenkins, MCP, Model Context Protocol, Arize, Braintrust, Datadog LLM monitoring, LangSmith, embedding models, retrieval strategies, prompt injection testing, guardrails, AWS Lambda, semantic caching, model routing, FinOps

Prepare for this role

Recommended resources to build the skills for this position. Sponsored.

Recommended Resources

• Top 20 LLM System Design Questions (Quick) – Zenaique – A focused LLM system design interview question set.
• Top 50 LLM System Design Interview Questions – Zenaique – Curated LLM system design questions from Zenaique.
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