VITOSHA

AI Developer

VITOSHAContract
Remote
10 - 15 YearsFeb 24th, 2026
11 ViewsBe an Early Applicant
Required Skillset:
PythonMachine LearningScikit-learnAgileScrumLoggingSecurityDatabasesDeep LearningMonitoringObservabilityPerformanceScalabilityReliabilityEmbeddingsPrompt TemplatesKnowledge RepositoriesDocumentsAPIsTensorFlowLarge Language Models (LLMs)Natural Language Processing (NLP)Test-Driven Development (TDD)Retrieval-Augmented Generation (RAG)Contextual Handling MechanismsService-Based ArchitecturesError-Handling

Job Description

We are looking for a skilled AI Developer to design, build, and deploy AI-driven and Generative AI capabilities within enterprise applications. This is a hands-on, execution-focused role involving development of intelligent services, integration with enterprise systems, and continuous optimization of AI solution quality, performance, and reliability.
The ideal candidate brings strong software engineering fundamentals, practical experience with AI/ML and Generative AI, and the ability to translate business requirements into production-ready AI solutions.
Key Responsibilities
AI & Application Development
Develop AI-enabled application features and services using modern programming practices.
Implement intelligent workflows processing structured and unstructured data.
Build reusable, modular, and scalable components across multiple use cases.
Generative AI Implementation
Develop applications leveraging Large Language Models (LLMs) and generative AI techniques.
Implement retrieval-based approaches (RAG) to improve response accuracy and relevance.
Design and optimize prompt templates, embeddings, and contextual handling mechanisms.
System Integration
Integrate AI capabilities into existing enterprise systems using APIs and service-based architectures.
Work with multiple data sources including documents, databases, and knowledge repositories.
Ensure AI services are secure, reliable, scalable, and performant.
Quality, Optimization & Reliability
Continuously improve AI output quality through tuning, evaluation, and testing.
Optimize latency, throughput, and operational cost of AI features.
Implement logging, monitoring, observability, and error-handling for production stability.
Collaboration & Delivery
Collaborate closely with architects, data engineers, product owners, and UX teams.
Contribute to proof-of-concepts (PoCs) and evolve them into scalable enterprise solutions.
Stay current with advancements in AI and apply innovations responsibly and ethically.

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