AI Agents Engineer
Bangalore, Karnataka, IndiaJobNo compensation foundPosted 1mo agoVerified open 5 days ago
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At a glance
Compensation
No compensation found
Location
Bangalore, Karnataka, India
Work Authorization
Not specified
Job overview
The AI Agents Engineer will design, build, and maintain production LLM-powered systems, integrating APIs, orchestration frameworks, and observability tools. Responsibilities include developing robust Python services, managing REST/webhook integrations, querying BigQuery for telemetry, and documenting designs with clear communication.
Skills & qualifications
RequiredNice to have
Skills
Quality AssuranceOrchestrationJiraBigQueryDockerRESTArtificial IntelligenceObservabilityPythonStreamingDatabase CachingCI/CDPagerDutyGithubSQLAnthropic ClaudeLangChainLlamaIndexDSPyCrewAIREST APIWebhook IntegrationSlackLinearNotionLoggingAlerts
Qualifications
3+ Years Software Engineering Experience1+ Year Production LLM-Powered Systems Experience
Full job description
Requirements:
- 3+ years of software engineering experience with at least 1 year building production LLM-powered systems or agents used by real users.
- Strong Python engineering skills; ability to write maintainable, tested, production-quality services.
- Hands-on experience with LLM APIs, preferably Anthropic Claude, including tool use/function calling, structured outputs, streaming, prompt caching, and multi-turn context handling.
- Experience with at least one agent/orchestration framework such as LangChain, LlamaIndex, DSPy, CrewAI, or strong custom orchestration experience.
- Strong REST API and webhook integration experience across tools such as GitHub, Slack, Jira/Linear, Notion, PagerDuty, or similar.
- Good understanding of LLM evaluation: defining success metrics, measuring false positives/negatives, creating feedback loops, and iterating from telemetry.
- Comfortable with Docker, CI/CD, observability, logging, alerts, and operating services in production.
- Strong SQL and BigQuery ability for querying logs, deploy events, incident timelines, accuracy signals, and time-series reliability data.
- Clear written communication; can write design docs that define scope, non-goals, evaluation methodology, owners, and risks.
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