Senior Software Engineer

Zingly; Bengaluru; On-site; INR 2,600,000 - 3,800,000; Full time

Job summary

Zingly is seeking a Senior Software Engineer who blends customer-facing skills with hands-on AI delivery. You will lead complex customer solutions from discovery through architecture, coding, agent configuration, production launch, and optimization.

Your work spans customer workflows, voice and digital agents, full-stack development, and enterprise integrations. You will turn deployment learnings into reusable platform capabilities and mentor others to strengthen customer partnerships.

Qualifications

• Production ownership: design, code, shipping and operating complex production software.
• Core programming languages: deep proficiency in Java, Python, Go, or Rust, and TypeScript frontend knowledge.
• AI implementation experience: hands-on with AI agents or LLM-powered apps in real customer workflows.
• Customer communication: lead discovery, demonstrate solutions, and manage expectations.
• Systems & integration: APIs, distributed systems, data modeling, asynchronous processing, enterprise integrations.

Responsibilities

• Lead technical discovery with stakeholders to uncover workflows and outcomes.
• Translate needs into architecture, plan, and commitments for customers.
• Build demos and prototypes, then production solutions.
• Write and review code for core services, integrations and agent tools.
• Design and deploy voice and digital agents, including prompts and flows.
• Architect CRM, contact center, helpdesk and core enterprise integrations.
• Decide between platform configuration, low-code, or custom development.
• Define evaluation and launch criteria including AI quality and security.
• Design controls for prompt injection, access control and data handling.
• Lead troubleshooting across models, prompts, code, and infra.
• Own go-live readiness and drive post-launch improvements.
• Mentor engineers and feed customer needs into product roadmap.

Skills

Backend languages

Frontend (TypeScript)

AI implementation

Customer communication

APIs & distributed systems

DevOps & observability

Delivery leadership

Team leadership

8+ years experience

Enterprise platforms

Tools

LangGraph

LangChain

LlamaIndex

OpenAI Agents SDK

Google ADK

Anthropic tooling

Semantic Kernel

AutoGen

Salesforce

HubSpot

Genesys

Twilio

NICE

Avaya

RAG

Pinecone

Elasticsearch/OpenSearch

Job description

We're looking for a Senior Software Engineer who combines strong customer-facing skills, hands-on AI implementation, and deep software engineering judgment. You'll lead complex customer solutions from discovery and demonstration through architecture, coding, agent configuration, production launch, and ongoing optimization. You must be able to explain a proposed solution clearly, build its critical components yourself, and stay accountable for how it performs.

Your work will span customer workflows, voice and digital agents, full-stack development, and enterprise integrations. You'll also turn lessons from deployments into reusable platform capabilities and help other engineers become stronger builders and customer partners.

Responsibilities:

• Technical discovery: Lead technical discovery with business and engineering stakeholders, uncovering workflow requirements, system constraints, and measurable outcomes.
• Architecture and commitments: Translate ambiguous needs into a practical architecture, implementation plan, and clear customer commitments.
• Demos and prototypes: Build demos and prototypes that validate the proposed approach, then develop them into maintainable production solutions.
• Code authorship: Personally write and review code for critical services, integrations, application features, and agent tools.
• Agent deployment: Design and deploy voice and digital agents, including prompts, conversation flows, retrieval, memory, orchestration, and human escalation.
• System integration: Architect integrations with CRM, contact center, helpdesk, and core enterprise systems, accounting for authentication, data mapping, retries, and partial failures.
• Solution trade-offs: Decide when to use platform configuration, low-code capabilities, or custom development, balancing speed with reliability and maintainability.
• Launch criteria: Define evaluation and launch criteria covering business task completion, AI quality, security, latency, cost, and operational readiness.
• Security and governance: Design controls for prompt injection, tool permissions, sensitive data, tenant isolation, and consequential agent actions.
• Complex troubleshooting: Lead complex troubleshooting across model behavior, prompts, application code, integrations, and infrastructure.
• Go-live ownership: Own technical readiness through go-live and stabilization, and use production evidence to improve customer outcomes.
• Mentorship and roadmap: Build reusable deployment patterns and platform capabilities, mentor engineers, and feed recurring customer needs into the product roadmap.

Requirements:

• Production ownership: A track record of designing, coding, shipping, and operating complex production software.
• Core programming languages: Deep proficiency in at least one backend language, such as Java, Python, Go, or Rust, and the ability to work across TypeScript-based frontend applications.
• AI implementation experience: Hands-on experience delivering AI agents or LLM-powered applications into real customer workflows.
• Customer communication: Strong customer-facing communication: you can lead discovery, demonstrate a solution, explain trade-offs, and manage technical expectations.
• Systems and integration expertise: Deep understanding of APIs, distributed systems, data modeling, asynchronous processing, and enterprise integrations.
• Agent orchestration: Practical experience with prompt design, tool use, retrieval, agent state, evaluations, and production failure handling.
• Engineering judgment: Sound judgment across security, scalability, reliability, performance, cost, and operational complexity.
• DevOps and observability: Experience with cloud platforms, CI/CD, automated testing, and observability.
• Delivery leadership: The ability to prioritize across complex implementations, surface risks early, and guide delivery while remaining hands-on.
• Team leadership: A record of mentoring engineers and improving the quality of both technical work and customer collaboration
• Experience: Typically 8+ years of relevant engineering experience, with demonstrated ownership of complex production systems and customer implementations.
• Enterprise platforms: Java/Spring, Salesforce, HubSpot, Genesys, Twilio, NICE, Avaya, or other CRM, CPaaS, and CCaaS platforms.
• Agent frameworks: LangGraph, LangChain, LlamaIndex, OpenAI Agents SDK, Google ADK, Anthropic agent tooling, Semantic Kernel, or AutoGen.
• Agent architecture: MCP, A2A, multi-agent orchestration, agent skills, memory/state, and durable execution.
• AI quality: Custom graders, LLM-as-judge, trajectory evaluations, LangSmith, Arize Phoenix, Braintrust, or Weights and Biases.
• Knowledge systems: RAG, reranking, hybrid search, GraphRAG, pgvector, Pinecone, Weaviate, or Elasticsearch/OpenSearch.
• Voice and real-time systems: WebRTC, WebSockets, OpenAI Realtime, streaming STT/TTS, Deepgram, ElevenLabs, or LiveKit.
• Infrastructure and security: AWS, GCP, Azure, Kubernetes, Terraform, agent sandboxing, identity controls, and regulated customer environments.
• AI-assisted development: Effective use of Claude Code, Codex, Cursor, or GitHub Copilot with rigorous verification.