We at Prometteur Solutions Pvt. Ltd. are a team of IT experts, who came with a promise of delivering technology-empowered business solutions. We provide world-class software and web development services that focus on playing a supportive role to your business and its holistic growth. Our highly-skilled associates and global delivery capabilities ensure the accessibility and scale to align client's technology solutions with their business needs. Our offerings span the entire IT lifecycle: from Consulting through Packaged, Custom, and Cloud Applications as well as a variety of Infrastructure Services.

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 goals

All your information will be kept confidential according to EEO guidelines.