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Posted 01 July, 2026

Generative AI Engineer

Aceolution
Hyderabad, TG, IN Full Time
Reference: 2890e470b0a013ef

Job Description

Job Description: Prompt Engineer\nEmployment Type: Full-time\nWork Type: Hybrid\nDuration: 12 months (Annual Renewal)\nLocation: Gurgaon or Hyderabad\n\nABOUT THE ENGAGEMENT:\nAceolution is hiring for the work that supports the development, evaluation, and safety calibration of frontier generative AI systems used by hundreds of millions of users worldwide.\n\nKEY RESPONSIBILITIES\nAs per the engagement scope: tuning and testing client APIs ,and running projects within the prompt-engineering pod. The work is likely to include agentic and tool-calling components. The day-to-day workflow runs from morning triage of overnight evaluation outputs, through hypothesis-driven prompt iteration, into regression testing and production deployment.\n\nPull and review overnight QA logs to identify discrepancies between the client tool orchestration layer’s outputs and the human-validated baseline.\nCategorize errors: hallucinations (model invented a rule), context misses (failed local language or cultural nuance), boundary failures (gray-area cases the model decided too rigidly).\nForm hypotheses on why the tool failed — cluttered context, missing few-shot examples, outdated RAG content — and refactor prompts to test the hypotheses.\nAdd negative constraints, update few-shot examples, request RAG layer updates as needed.\nRun regression tests against the Golden Dataset before deploying any prompt change.

Confirm fixes do not silently regress other workflows.\nDeploy approved prompt changes to the live orchestration layer and monitor output for stability.\nAuthor hypothesis docs, contribute to weekly RCA reports, and participate in client calibration sessions.\n\nMUST-HAVE SKILLS AND EXPERIENCE\nHands-on production experience with at least one major LLM API — Gemini, OpenAI, or Anthropic. Specific Gemini API experience is strong-to-have but not mandatory; the patterns are highly transferable and a strong candidate from another LLM platform.\nMust be able to discuss prompt strategies fluently — context window management, few-shot construction, system prompt design, structured output. Not just “I have used ChatGPT”.\nStrong analytical reasoning and structured problem-solving.

The work is hypothesis → experiment → regression test → deploy → monitor.\nPython fluency — this is non-negotiable. You read, write, modify, and debug Python daily. These are the engineering workhorses of the pod, not prompt hobbyists; “I can run a notebook” is not enough.\nComfortable with object-oriented Python.

The agent and tool-calling frameworks in this space are class-based, so you need to read and extend OOP code, not just write top-to-bottom scripts. You don’t need to be a systems architect, but a scripts-only background will not clear the bar.\nStrong written communication. Daily output includes hypothesis docs, RCA contributions, and inputs to the client-facing weekly review.\nHigh tolerance for ambiguity.

The work involves gray-area judgements where guidelines are 50% indicative; you must be comfortable making subjective decisions and defending them.\nLinguistic precision and cultural awareness.\n\nSTRONG-TO-HAVE SKILLS\nPrior experience with prompt-engineering patterns: chain-of-thought, few-shot, ReAct, structured output.\nFamiliarity with eval datasets, labelled-data workflows, or human-in-the-loop systems.\nBackground in content moderation, ad evaluation, or trust-and-safety operations.\nFamiliarity with API tools (Postman) and basic understanding of REST APIs and JSON.\nMultilingual fluency, especially in any major non-English market language.\n\nImportant notice:\nAceolution Inc. will never request a monetary deposit for any role or project with the company, and our recruitment and sourcing teams only use @aceolution.com address when emailing candidates. Ignore aceolutions.com which is a spammer email ID doing rounds over the past few months.

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