AI LLM Integration Developer

Job Description -

Need the Best Comm. And technical skills
AI LLM Integration Developer (PV W2) 100% Interview

We are seeking a skilled and innovative AI LLM Integration Developer to join our growing team. In this role, you will be responsible for integrating Large Language Models (LLMs) into our products and services, enabling intelligent, context-aware, and scalable AI-powered solutions. The ideal candidate has a strong background in software development, experience working with APIs for foundation models (e.g., OpenAI, Anthropic, Google Gemini), and a deep understanding of prompt engineering, data pipelines, and real-world AI applications.

Key Responsibilities:

• LLM Integration: Design, develop, and deploy applications that interface with LLMs via APIs (e.g., OpenAI, Cohere, Mistral, Hugging Face).
• Prompt Engineering: Create and optimize prompt strategies for specific use cases, ensuring accurate and reliable LLM outputs.
• System Architecture: Architect scalable, secure, and efficient solutions that incorporate LLMs into existing systems or new products.
• Tooling & Automation: Develop internal tools and workflows to facilitate testing, evaluation, and deployment of LLM-based features.
• Data Integration: Work with structured and unstructured data sources to support context-aware applications (e.g., RAG pipelines, vector databases).
• Collaboration: Work closely with product managers, UX designers, data engineers, and ML researchers to align AI solutions with business goals.
• Monitoring & Maintenance: Implement logging, error handling, and monitoring for AI-powered services in production.

Required Qualifications:

• Bachelor s degree in Computer Science, Software Engineering, or related field (or equivalent experience).
• 3+ years of software development experience.
• Proficiency in Python and/or JavaScript (Node.js), with strong API development experience.
• Familiarity with LLM platforms (e.g., OpenAI GPT, Claude, Gemini, LLaMA).
• Experience with prompt design, fine-tuning, or retrieval-augmented generation (RAG) techniques.
• Understanding of vector databases (e.g., Pinecone, Weaviate, FAISS) and embeddings.
• Experience with cloud platforms (AWS, Google Cloud Platform, or Azure).
• Strong problem-solving and communication skills.