Job Description AI Developer
Role: AI Developer / AI Engineer
Experience: 3–6 Years (suggested)
Technical Focus: AI/ML, Deep Learning, NLP, Generative AI, Python (FastAPI), Node.js (Express.js), LLM Integration and AI-powered Web Applications
Key Responsibilities & Required Skills
1. AI Application Development: Design, develop and integrate AI-powered processes and intelligent features into web applications using industry-standard AI frameworks, tools and libraries.
2. Programming & API Development: Solid hands-on experience in Python and Node.js for developing AI services, backend integrations, APIs, automation workflows and AI-driven application components. Proven experience building high-performance REST APIs and AI microservices using FastAPI (Python) and Express.js (Node.js), including request/response validation, asynchronous processing, authentication, OpenAPI/Swagger documentation, and serving ML/LLM models as scalable API endpoints.
3. Machine Learning, Deep Learning & NLP: Solid foundation in Machine Learning, Deep Learning and Natural Language Processing, with hands-on experience in data preparation, feature engineering, model training, fine-tuning and evaluation. Practical experience with NLP tasks such as text classification, named entity recognition, information extraction, summarization, semantic search and sentiment analysis, using libraries such as scikit-learn, PyTorch, TensorFlow/Keras, Hugging Face Transformers, spaCy and NLTK.
4. Generative AI & LLM Integration: Practical experience working with LLMs such as OpenAI GPT, Google Gemini, Anthropic Claude, Llama and other open-source or commercial AI models.
5.



AI Frameworks & Libraries: Experience with industry-standard Generative AI frameworks and libraries such as LangChain, LangGraph, LlamaIndex, Hugging Face and relevant AI/ML ecosystems.
6. AI Model Evaluation & Selection: Ability to evaluate different AI adoption approaches, models and architectures, assess their suitability against business requirements, and recommend cost-effective and scalable solutions.
7. End-to-End AI Implementation: Ability to independently drive AI adoption from requirement analysis and solution design to POC development, model evaluation, integration, deployment and production rollout.
8. POC to Production-grade Development: Strong capability to convert experimental AI prototypes into reliable, secure, scalable and maintainable production-grade solutions, including error handling, performance optimization and monitoring.
9. AI Integration & Architecture: Experience integrating AI capabilities with existing web applications, REST APIs, databases and enterprise systems. Understanding of AI agents, workflow automation, function/tool calling and orchestration is desirable.
10. Responsible AI & Continuous Improvement: Understanding of AI security, data privacy, prompt engineering, hallucination mitigation, model limitations, token/cost optimization and continuous evaluation of AI-powered processes.
Good to Have
Exposure to additional AI adoption approaches such as Computer Vision and Agentic AI.
Exposure to Docker, Kubernetes and CI/CD for deploying and managing AI services.
Experience with vector databases such as PostgreSQL with pgvector, Pinecone, Weaviate or Milvus.

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