Generative AI Developer

Job Description

Job Description

Generative AI Developer Job Description Job Title

Generative AI Developer

Location

[City/Remote/Hybrid]

Employment Type

Full-time / Contract

Job Summary

We are seeking a Generative AI Developer to design, develop, and deploy AI-powered applications using large language models (LLMs), multimodal AI models, and modern AI frameworks. The ideal candidate will have experience building production-ready generative AI solutions, integrating AI services into enterprise applications, and optimizing model performance, reliability, and security. This role requires strong software engineering skills, expertise in AI frameworks, and the ability to collaborate with cross-functional teams to deliver innovative AI solutions.

Key Responsibilities

• Design, develop, and deploy generative AI applications using LLMs and multimodal AI models.
• Build AI-powered chatbots, virtual assistants, copilots, document intelligence solutions, and content generation applications.
• Develop Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise knowledge sources.
• Design and optimize prompts, system instructions, and workflows to improve model performance and accuracy.
• Integrate foundation models through APIs and cloud AI platforms into web, mobile, and enterprise applications.
• Fine-tune, evaluate, and optimize AI models where appropriate using modern training and evaluation techniques.
• Develop AI agents capable of planning, reasoning, and orchestrating multi-step tasks.
• Implement AI safety measures, content filtering, guardrails, and responsible AI practices.
• Collaborate with data engineers, software developers, DevOps engineers, product managers, and business stakeholders.
• Monitor AI application performance, latency, quality, and cost, and continuously improve deployed solutions.
• Create technical documentation, reusable components, and best practices for AI application development.
• Stay current with advancements in generative AI, foundation models, agentic AI, and emerging frameworks.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.
• 3 8 years of software development experience, including hands-on experience with generative AI technologies.
• Strong proficiency in Python and experience with modern software development practices.
• Experience working with large language models (LLMs) and foundation model APIs.
• Hands-on experience with prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, and vector databases.
• Experience developing REST APIs and integrating AI services into enterprise applications.
• Knowledge of machine learning fundamentals, NLP, and model evaluation techniques.
• Familiarity with cloud platforms and AI services.
• Strong analytical, debugging, and problem-solving skills.

Preferred Qualifications

• Experience building production-scale AI applications.
• Experience with agentic AI architectures and workflow orchestration.
• Knowledge of model fine-tuning, parameter-efficient tuning, or reinforcement learning concepts.
• Experience with multimodal AI applications involving text, images, audio, or video.
• Experience with MLOps, CI/CD pipelines, and containerized deployments.
• Contributions to open-source AI projects or published AI research.
• Relevant cloud or AI certifications.

Technical Skills

• Python
• SQL
• Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• AI Agents
• LangChain
• LlamaIndex
• Semantic Kernel
• Hugging Face Transformers
• OpenAI API
• Anthropic API
• Google Gemini API
• Embeddings
• Vector Databases (Pinecone, Weaviate, Chroma, FAISS, Milvus)
• TensorFlow or PyTorch
• FastAPI or Flask
• REST APIs
• Docker
• Kubernetes
• Git
• CI/CD
• Azure AI, AWS Bedrock, or Google Vertex AI
• Redis
• Elasticsearch

Soft Skills

• Problem-solving
• Collaboration
• Communication
• Analytical thinking
• Innovation
• Adaptability
• Time management
• Documentation skills
• Continuous learning

Key Deliverables

• Production-ready generative AI applications
• AI chatbots and virtual assistants
• RAG pipelines and enterprise search solutions
• AI agents and workflow automation
• Prompt libraries and reusable AI components
• API integrations and microservices
• Technical documentation
• Performance monitoring dashboards
• AI evaluation and testing reports

Success Metrics

• Successful deployment of production AI solutions
• Model response quality, accuracy, and relevance
• Reduced latency and infrastructure costs
• User adoption and satisfaction
• Reliability and availability of AI services
• Compliance with security and responsible AI standards
• On-time delivery of AI features and enhancements
• Continuous improvement based on evaluation metrics and user feedback