AI Engineer

Company Description

Prometteur Solutions delivers cutting-edge technology-driven business solutions tailored for organizations across enterprise, government, and education markets. Committed to client satisfaction, the company combines highly-skilled professionals with global delivery capabilities to align technology solutions with business goals. With expertise in areas such as Enterprise Mobility, Web Development, UX/UI Design, and Software Consulting, Prometteur Solutions partners with a diverse range of industries including E-Commerce, Healthcare, Education, Media, and more. Leveraging modern technologies like React Native, Flutter, Node.js, and PHP, the companys mission is to build long-term client relationships by delivering meaningful and lasting business value.

• Job Title:
• AI Engineer (Applied AI / LLM)
• Experience: 34 Years
• Employment Type: Full-Time
• Role Overview
• We are looking for an AI Engineer with 2 years of hands-on experience building and delivering
• production-ready AI solutions for real customers. This role is focused on Applied AI, meaning
• building, shipping, and maintaining AI-powered features. The ideal candidate has a strong
• foundation in AI/ML concepts, solid software engineering skills, and proven experience integrating
• LLMs and AI services into scalable applications. You will work closely with product and
• engineering teams to design, develop, and operate AI features that solve real business problems.
• Key Responsibilities
• AI Solution Design & Delivery
• ? Understand business requirements and translate them into practical AI-driven solutions
• ? Design and implement end-to-end AI workflows using existing models, SDKs, and APIs
• ? Deliver customer-facing AI features with a strong focus on reliability, usability, and
• performance
• Backend & Integration
• ? Build backend services and APIs for AI modules (language/framework agnostic)
• ? Integrate AI services with existing systems, databases, and third-party tools
• ? Collaborate with frontend engineers to ensure smooth integration and user experience
• Quality, Reliability & Optimization
• Improve AI output quality using:
• ? Prompt engineering and prompt versioning
• ? Structured outputs (JSON, schema-based responses)
• ? Guardrails, validations, and fallbacks
• Ensure production readiness by addressing:
• ? Performance and latency optimization
• ? Error handling, retries, monitoring
• ? Cost optimization (token usage, caching, API efficiency)
• Security & Maintainability
• ? Follow responsible AI practices including data privacy and security basics
• ? Maintain clean, well-documented, versioned AI components
• ? Design systems that allow easy model/provider replacement when needed
• Required Skills
• Core Engineering Skills
• ? Strong software engineering fundamentals (clean code, modularity, testing, API design)
• ? Experience building production-grade backend services in any modern backend stack
• ? Understanding of async/non-blocking patterns and scalable service design
• ? Robust error handling and integration readiness
• AI/ML & LLM Experience
• ? Hands-on experience using LLM APIs such as OpenAI, Gemini, Anthropic, or similar
• ? Experience using frameworks/tools such as LangChain, LlamaIndex, or equivalents
• ? Strong understanding of prompt engineering and evaluation methodologies
• Data & Storage
• ? Experience working with unstructured datasets (PDFs, documents, text, OCR outputs)
• ? Experience with vector stores/vector DBs: Pinecone, Weaviate, Chroma, FAISS, etc.
• ? Working knowledge of relational DB concepts (SQL basics)
• Backend Development
• ? Experience building secure APIs (REST/GraphQL) with authentication and integrations
• ? Ability to integrate AI modules into microservices / monolith architectures
• Must-Have Experience
• ? 2 years in applied AI, AI engineering, or ML-driven software development
• ? Proven record of delivering AI features used by real customers in production
• ? Ability to share case studies demonstrating:
• ? Problem statement
• ? Solution approach
• ? Technology stack
• ? Business impact / user outcomes
• Nice to Have
• ? Experience with OCR and document intelligence systems
• ? Speech-to-text and text-to-speech exposure
• ? Experience deploying systems on AWS/GCP/Azure
• ? Containerization experience (Docker fundamentals)
• ? Exposure to monitoring/logging/MLOps best practices
• What Success Looks Like
• ? AI features are deployed and actively used by customers
• ? Continuous improvement in AI quality, reliability, and cost efficiency
• ? AI systems are scalable, maintainable, and aligned with product goal