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Hyderabad, Ind-36, IN, 500032
Live, first seen by us 17 days agoImplement AI-infused feature slices - prompt templates, RAG retrieval, and tool-calling -against agreed acceptance criteria.
Monitor features for latency, token cost, and error rates, and tune prompts and context to meet performance budgets.
Key Responsibilities: Implement AI-infused feature slices - prompt templates, RAG retrieval, and tool-calling -against agreed acceptance criteria. Use GitHub Copilot and AI-assisted code generation to scaffold and refactor code, reviewing every suggestion for correctness and security before integration.
Validate model outputs for grounding, format, and safety, and add regression checks that catch hallucination and drift. Monitor features for latency, token cost, and error rates, and tune prompts and context to meet performance budgets. Debug non-deterministic failures across the prompt–retrieval–tool chain and implement error-handling fallbacks.
Experience: 3-5 Years
Mandatory Skills: GCP BigQuery
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Day 17
Similar jobs usually come down between day 4 and day 77.
From 51 similar postings that came down. Coming down is not always a hire.
173 open jobs
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Working understanding of AI application development patterns— prompt orchestration, retrieval-augmented generation (RAG), and tool-calling. Hands-on coding ability in a mainstream stack (Python, TypeScript/Node, or Java) and familiarity using AI pair-programming assistants such as GitHub Copilot. Familiarity with integrating foundation-model APIs into application back-ends via SDKs, function/tool schemas, and orchestration frameworks.
͏ Mandatory Skills: GCP BigQuery . Experience: 3-5 Years .
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We file this job under AI Engineer, by the work the posting describes.
AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.
Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.
An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.
Job Function:
Builds AI-infused, user-facing applications by integrating foundation models, RAG, agentic patterns, and AI-assisted development into production software.
Core skills: AI-Native Engineering; Generative AI Prompt Engineering; AI assisted code generation
Qualifications and Skills:
Working understanding of AI application development patterns— prompt orchestration, retrieval-augmented generation (RAG), and tool-calling.
Hands-on coding ability in a mainstream stack (Python, TypeScript/Node, or Java) and familiarity using AI pair-programming assistants such as GitHub Copilot.
Familiarity with integrating foundation-model APIs into application back-ends via SDKs, function/tool schemas, and orchestration frameworks.
Basic knowledge of designing microservices and REST/GraphQL APIs that expose AI capabilities to front-end and downstream consumers.
Ability to validate AI outputs for correctness, grounding, and safety, and to instrument latency, token cost, and failure modes.
Experience Requirements:
Has built or contributed to at least one AI-enabled application, feature, RAG assistant or agentic workflow through academic, personal or professional projects.
Experience using AI-assisted coding tools to accelerate software development while validating generated code for correctness.
Familiarity with integrating LLM or foundation model APIs into applications.
Exposure to Agile development practices, Git, CI/CD and code review processes.
Exposure to debugging AI application behaviour, including prompt failures, hallucinations and response quality issues.
Key Responsibilities:
Implement AI-infused feature slices - prompt templates, RAG retrieval, and tool-calling -against agreed acceptance criteria.
Use GitHub Copilot and AI-assisted code generation to scaffold and refactor code, reviewing every suggestion for correctness and security before integration.
Integrate foundation-model APIs into microservices and expose them through clean REST/GraphQL APIs
Validate model outputs for grounding, format, and safety, and add regression checks that catch hallucination and drift.
Monitor features for latency, token cost, and error rates, and tune prompts and context to meet performance budgets.
Debug non-deterministic failures across the prompt–retrieval–tool chain and implement error-handling fallbacks.
Mandatory Skills: GCP BigQuery .
Experience: 3-5 Years .
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Hyderabad, Ind-36, IN, 500032
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