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We are seeking an creative Generative AI Engineer with strong expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Python, and Machine Learning. The ideal candidate will be responsible for designing, developing, and deploying AI-powered applications that leverage state-of-the-art language models to solve complex business problems. The candidate should have hands-on experience with LLM integration, prompt engineering, vector databases, RAG pipelines, machine learning model development, and cloud-based AI services. This role requires close collaboration with data scientists, software engineers, product teams, and business stakeholders to build scalable AI solutions. Key ResponsibilitiesGenerative AI Development - Design, develop, and deploy Generative AI solutions using modern LLM frameworks. - Build AI-powered applications such as intelligent chatbots, virtual assistants, document processing systems, and content generation platforms. - Integrate foundation models through APIs and open-source frameworks. - Optimize AI applications for scalability, performance, and cost efficiency. - Stay updated with the latest advancements in Generative AI technologies. Large Language Models (LLMs) - Develop applications utilizing commercial and open-source LLMs. - Fine-tune, evaluate, and optimize language models where applicable. - Design effective prompt engineering strategies for improved model responses. - Implement model monitoring, evaluation, and response quality metrics. - Address AI safety, hallucination reduction, and responsible AI practices. Retrieval-Augmented Generation (RAG) - Design and implement Retrieval-Augmented Generation (RAG) pipelines. - Build document ingestion, embedding, indexing, and retrieval workflows. - Integrate vector databases for semantic search and knowledge retrieval. - Optimize retrieval accuracy, context management, and response relevance. - Work with structured and unstructured enterprise data sources. Machine Learning & Data Science - Develop, train, evaluate, and deploy machine learning models. - Perform data preprocessing, feature engineering, and model validation. - Implement NLP and text analytics solutions. - Analyze model performance and improve prediction accuracy. - Collaborate with data engineering teams to build scalable ML pipelines. Python Development - Develop scalable backend services and AI applications using Python. - Build REST APIs for AI model integration. - Write clean, modular, and maintainable production-grade code. - Develop reusable libraries and automation scripts. - Integrate AI solutions with enterprise applications. Deployment & MLOps - Deploy AI models to cloud environments and production systems. - Implement CI/CD pipelines for machine learning workflows. - Monitor model performance, usage, and reliability. - Support containerization using Docker and orchestration with Kubernetes (preferred). - Maintain model versioning and experiment tracking. Collaboration & Innovation - Collaborate with product managers, engineers, and business stakeholders. - Participate in architecture discussions and AI solution design. - Conduct proof-of-concepts (POCs) for emerging AI technologies. - Mentor junior developers and promote AI best practices. Required SkillsGenerative AI - Generative AI - Large Language Models (LLMs) - Prompt Engineering - AI Agents - Responsible AI RAG & Knowledge Retrieval - Retrieval-Augmented Generation (RAG) - Vector Databases - Semantic Search - Embeddings - Document Processing Machine Learning - Machine Learning - Natural Language Processing (NLP) - Deep Learning - Model Training & Evaluation - Feature Engineering Programming - Python - FastAPI / Flask - REST APIs - Object-Oriented Programming AI Frameworks - LangChain - LlamaIndex - Hugging Face Transformers - OpenAI APIs - PyTorch - TensorFlow - Scikit-learn Databases - PostgreSQL - MongoDB - Redis - Vector Databases (Pinecone, ChromaDB, FAISS, Weaviate, or Milvus) Cloud & DevOps - Microsoft Azure / AWS / Google Cloud Platform (GCP) - Docker - Kubernetes - Git - CI/CD Pipelines Preferred Qualifications - Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field. - 48 years of experience in Python development and Machine Learning. - Hands-on experience building Generative AI and RAG-based applications. - Strong understanding of NLP, embeddings, vector search, and LLM architectures. - Experience deploying AI applications in cloud environments. - Familiarity with MLOps practices, model monitoring, and experiment tracking. - Excellent analytical, problem-solving, and communication skills. - Experience working in Agile/Scrum development environments. Preferred Certifications - Microsoft Certified: Azure AI Engineer Associate (AI-102) - Microsoft Certified: Azure Data Scientist Associate (DP-100) - AWS Certified Machine Learning Specialty - Google Professional Machine .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.