Job Description: Generative AI EngineerJob 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 SummaryWe 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.
EducationBachelor'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.
