Gen AI Developer
At a glance
Mid-level AI Engineering role at Fa Ewjt Saasfaprod1. Noida · full-time.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and launch GenAI applications with LLM platforms, APIs and orchestration frameworks
- Turn business needs into scalable AI features, reusable software components and production deployments
- Create and refine prompts, templates and response checks to improve output quality
- Develop RAG workflows for document ingestion, indexing and retrieval using embeddings and vector databases
- Expose AI services through REST APIs and microservices, then test and monitor their reliability and performance
What you bring
- A bachelor's degree in computer science, data science or another listed field
- Three to six years developing software, including at least one to two years focused on generative AI
- Working knowledge of LLMs, NLP, prompt design, Python and AI development frameworks
- Experience with LangChain or a comparable orchestration framework, plus APIs and microservices
- Practical RAG experience with embeddings and vector databases
Who this fits
This mid-level role suits a developer who has shipped generative AI solutions and can work across prompts, retrieval and backend services. It is based in Noida, India, with a hybrid arrangement.
From the employer
We are seeking a talented Generative AI Developer to design, develop, and deploy AI-powered applications leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and modern AI orchestration frameworks. The ideal candidate will have hands-on experience building AI workflows, developing reusable prompt-based solutions, integrating vector databases, and exposing AI capabilities through scalable APIs and microservices.
This role will work closely with AI architects, data engineers, product owners, and business stakeholders to build innovative GenAI applications that solve real-world business problems while ensuring reliability, scalability, and performance.
AI Application Development
- Develop and deploy GenAI applications using leading LLM platforms and APIs.
- Create intelligent workflows and AI-powered business solutions leveraging modern orchestration frameworks.
- Translate business requirements into scalable AI features and reusable software components.
- Participate in the full development lifecycle from prototyping through production deployment.
Prompt Engineering & LLM Integration
- Design, test, and optimize prompts for consistent and high-quality LLM outputs.
- Develop reusable prompt templates and structured response frameworks.
- Implement techniques for output control, response validation, and prompt optimization.
- Support prompt testing and tuning to improve model performance and user experience.
RAG & Knowledge Retrieval Solutions
- Develop Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge applications.
- Integrate vector databases, embeddings, and semantic search capabilities.
- Build document ingestion, indexing, chunking, and retrieval workflows.
- Optimize retrieval quality, relevance, and response accuracy.
API & Microservices Development
- Design and build RESTful APIs and microservices to expose AI capabilities.
- Integrate GenAI solutions with enterprise applications, databases, and third-party platforms.
- Develop scalable backend services supporting AI-driven use cases.
- Ensure security, maintainability, and performance of deployed AI services.
Testing & Quality Assurance
- Develop testing strategies for prompts, workflows, and AI-generated outputs.
- Utilize synthetic datasets to validate AI use cases and edge cases.
- Monitor solution accuracy, reliability, and business performance metrics.
- Support evaluation and benchmarking activities for AI applications.
Collaboration & Innovation
- Work closely with AI Architects and Senior GenAI Engineers on enterprise AI initiatives.
- Participate in AI accelerators, reusable framework development, and innovation programs.
- Stay current with developments in LLMs, NLP, prompt engineering, and agentic AI technologies.
- Contribute to AI best practices, coding standards, and knowledge-sharing initiatives.
- Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field.
- 3–6 years of software development experience with at least 1–2 years focused on Generative AI solutions.
- Strong foundation in Large Language Models (LLMs), NLP, prompt engineering, and AI application development.
- Proficiency in Python and AI development frameworks.
- Experience using LangChain or similar frameworks for workflow orchestration.
- Experience developing prompt templates, structured outputs, and reusable AI components.
- Hands-on experience building APIs and microservices for AI-driven applications.
- Experience with embeddings, vector databases, and Retrieval-Augmented Generation (RAG) architectures.
- Familiarity with synthetic data generation and AI testing methodologies.
- Strong problem-solving and debugging skills.