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As a Release Readiness Agent, your main role will involve building a custom GPT-based agent to assess code changes, build artifacts, and test outputs for determining production readiness. Additionally, you will act as an Automated RCA Agent by processing logs, alerts, and incident artifacts to create initial RCAs. You will be tasked with utilizing telemetry data from New Relic to develop Predictive Health Models that can predict issues before they lead to outages. Furthermore, you will be responsible for designing LLM-powered retrieval workflows to aid in ticket triage and operational inquiries.Your qualifications for this role should include at least 7 years of engineering experience, with a minimum of 2 years dedicated to constructing production-grade LLM applications. You must possess expertise in Python and contemporary agent frameworks such as LangChain, LlamaIndex, OpenAI function calling, or their equivalents. Experience in integrating with CI/CD outputs, logs, monitoring tools, and ticketing systems is essential. A solid comprehension of RAG, vector indexing, and prompt engineering best practices is also required.In this position, you will be expected to deliver end-to-end solutions, encompassing design, development, deployment, and evaluation. It is important to note that this is not a traditional ML modeling role but rather a hands-on position involving LLM/agent experience. The ability to operate independently and swiftly produce PoCs (Proof of Concepts) is crucial for success in this role. As a Release Readiness Agent, your main role will involve building a custom GPT-based agent to assess code changes, build artifacts, and test outputs for determining production readiness. Additionally, you will act as an Automated RCA Agent by processing logs, alerts, and incident artifacts to create initial RCAs. You will be tasked with utilizing telemetry data from Current Relic to develop Predictive Health Models that can predict issues before they lead to outages. Furthermore, you will be responsible for designing LLM-powered retrieval workflows to aid in ticket triage and operational inquiries.Your qualifications for this role should include at least 7 years of engineering experience, with a minimum of 2 years dedicated to constructing production-grade LLM applications. You must possess expertise in Python and contemporary agent frameworks such as LangChain, LlamaIndex, OpenAI function calling, or their equivalents. Experience in integrating with CI/CD outputs, logs, monitoring tools, and ticketing systems is essential. A solid comprehension of RAG, vector indexing, and prompt engineering best practices is also required.In this position, you will be expected to deliver end-to-end solutions, encompassing design, development, deployment, and evaluation. It is important to note that this is not a traditional ML modeling role but rather a hands-on position involving LLM/agent experience. The ability to operate independently and swiftly produce PoCs (Proof of Concepts) is crucial for success in this role. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.