Senior Engineer AI-Assisted Development

Sonrisa; India; Hybrid; INR 4,000,000 - 7,000,000; Full time

Benefits offered by this job

Flexible schedule

International projects

Home office kit

Healthcare

Job summary

Sonrisa seeks a Senior Engineer to thrive in AI-augmented development environments. You will collaborate within hybrid teams, combining hands-on coding with AI-assisted execution to deliver high-quality software.

Responsibilities include setting technical direction, validating AI outputs, and translating client needs into robust solutions while driving best practices for human+AI workflows.

Qualifications

• Hands-on experience with Java/.NET and frontend frameworks.
• Experience with AI-augmented development is a plus in hybrid teams.
• Ability to translate business needs into technical solutions.

Responsibilities

• Work in hybrid teams where humans and AI agents collaborate in delivery workflows.
• Set direction and technical strategy; ensure quality of AI-assisted outputs.
• Manage client relationships and translate business needs into solutions.
• Guide implementation with hands-on development and AI-assisted execution.

Skills

Java/.NET experience

Tools

GitHub Copilot

Cursor IDE

Windsurf (Codeium)

JetBrains AI Assistant

Continue.dev

Claude Code

Kilo Code

OpenCode

GitHub Copilot Agent mode

Devin SWE-agent

Job description

Job Description

We are looking for a Senior Engineer who is comfortable working in AI-augmented development environments and can effectively leverage modern coding assistants and agentic tools to deliver high-quality software.

WHAT YOULL DO

• Work in hybrid engineering teams where humans and AI agents collaborate as part of the same delivery workflow
• Stay in the loop for key responsibilities:
  • Setting direction and technical strategy
  • Quality assurance and validation of AI-assisted outputs
  • Critical decision-making and maintaining accountability for outcomes
  • Managing client relationships and translating business needs into solutions
• Guide implementation by combining hands-on development with AI-assisted execution
• Break down complex problems into structured tasks that can be executed by the hybrid team
• Contribute to defining best practices and workflows for effective human + AI collaboration
• Identify opportunities where AI can accelerate delivery while ensuring human expertise drives reliability

WE HAVE MANY THINGS TO OFFER

Flexible schedule, international projects, home office kit, healthcare and more, you name it. Check out the whole list of benefits on our dedicated page.

MUST HAVE

• Hands-on experience with Java / .NET / Frontend frameworks

NICE TO HAVE

AI-Augmented Development Tools

• GitHub Copilot (inline completion + Copilot Chat / Copilot Agent mode)
• Cursor IDE — agentic, multi-file editing with natural language instructions
• Windsurf (Codeium) — agentic coding with Cascade
• JetBrains AI Assistant — relevant for Java/.NET leads already on IntelliJ/Rider
• Continue.dev — open-source, self-hosted alternative (relevant for security-conscious clients)

Agentic coding / task automation

• Claude Code — terminal-based agentic coding
• Kilo Code — VS Code extension for agentic, multi-step coding tasks; open-source fork of Cline with strong local model support
• OpenCode — terminal-native AI coding agent, model-agnostic; relevant for engineers who prefer CLI-first workflows or self-hosted setups
• GitHub Copilot Agent mode
• Devin, SWE-agent (awareness-level)

Practices & Mindset (the more differentiating signal)

• Prompt engineering for code generation — writing effective, context-rich prompts; iterating on AI output rather than accepting it blindly
• AI-assisted code review — using LLM tools to pre-screen PRs, catch patterns, suggest refactors
• Test generation with AI — leveraging Copilot/Cursor to scaffold unit and integration tests
• Context engineering — structuring repos, READMEs, and architecture docs so AI tools can reason over them effectively (this is the senior/lead differentiator)
• Agentic workflow design — ability to break tasks into agent-executable steps; understanding when to use human-in-the-loop vs. autonomous execution

LLM & AI Platform Familiarity

• OpenAI API / Azure OpenAI
• Anthropic API (Claude)
• AWS Bedrock or Google Vertex AI (for DevOps/cloud leads)
• LangChain or LlamaIndex — basic understanding of RAG and chain patterns

AI-Augmented DevOps / Platform (for DevOps leads specifically)

• AI-assisted IaC generation (Copilot + Terraform, Pulumi AI)
• GitHub Actions with AI steps or LLM-based pipeline stages
• Monitoring/observability tools with AI anomaly detection (Datadog AI, AWS DevOps Guru)

Requirements