Generative AI Engineer

Job Description

Job Description

Job Description: Generative AI Engineer

Job Title: Generative AI Engineer
Experience: 3–6 Years (with relevant GenAI experience)
Employment Type: Full-Time
Location: Hybrid / Remote / On-site
Department: AI / Machine Learning / Engineering

Job Summary

We are looking for a skilled Generative AI Engineer to design, develop, and deploy AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and modern AI frameworks. The ideal candidate will have strong Python programming skills, experience integrating LLMs into production applications, and a solid understanding of machine learning, NLP, and cloud-based AI solutions.

Key Responsibilities

• Design, develop, and deploy Generative AI applications using LLMs such as GPT, Claude, Gemini, and open-source models.
• Build and optimize RAG pipelines, including document ingestion, chunking, embedding generation, retrieval, reranking, and response generation.
• Develop AI agents and multi-agent workflows using frameworks such as LangChain, LangGraph, or Semantic Kernel.
• Integrate vector databases such as Pinecone, FAISS, Chroma, or Weaviate.
• Implement prompt engineering, context management, and model evaluation techniques to improve response quality.
• Develop REST APIs and backend services using Python and frameworks such as FastAPI or Flask.
• Work with structured and unstructured data, including PDFs, documents, databases, and enterprise knowledge bases.
• Implement responsible AI practices, including guardrails, hallucination mitigation, security, and data privacy.
• Optimize model performance, latency, token consumption, and inference costs.
• Deploy and monitor AI applications using cloud platforms and MLOps/LLMOps practices.
• Collaborate with data scientists, software engineers, product managers, and business stakeholders.

Required Skills and Qualifications

• Strong programming experience in Python.
• Hands-on experience building Generative AI applications using commercial or open-source LLMs.
• Strong understanding of LLM architecture, transformer models, tokenization, embeddings, and context windows.
• Practical experience implementing RAG-based applications.
• Experience with prompt engineering, few-shot learning, and structured output generation.
• Familiarity with vector databases and semantic search.
• Experience with APIs, microservices, and backend development.
• Knowledge of NLP, machine learning, and deep learning fundamentals.
• Familiarity with Git, Docker, CI/CD, and software development best practices.
• Experience with at least one cloud platform: AWS, Azure, or Google Cloud.

Preferred Skills

• Experience building AI agents, tool-calling workflows, and multi-step reasoning applications.
• Familiarity with LangChain, LangGraph, LlamaIndex, or Semantic Kernel.
• Experience with fine-tuning, LoRA/QLoRA, or parameter-efficient model adaptation.
• Knowledge of LLM evaluation frameworks and observability tools.
• Experience with Hugging Face Transformers, PyTorch, or TensorFlow.
• Familiarity with Kubernetes, MLflow, and production model-serving systems.
• Experience implementing AI safety, access controls, and enterprise-grade security.

Education

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field, or equivalent practical experience.

Key Deliverables

• Production-ready Generative AI applications.
• Scalable RAG pipelines and enterprise knowledge assistants.
• Reliable AI agents and automated workflows.
• Measurable improvements in response accuracy, latency, and operational cost.
• Well-documented, secure, and maintainable AI services.

Success Criteria

• Quality, relevance, and factual accuracy of AI-generated responses.
• Retrieval accuracy and reduction in hallucinations.
• Application performance, reliability, and scalability.
• Cost-effective model usage and efficient inference.
• Successful integration of AI solutions into business workflows.