Job Description
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 company’s mission is to build long-term client relationships by delivering meaningful and lasting business value.
Job Title:
AI Engineer (Applied AI / LLM)
Experience: 3–4 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
