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Project Description: We are seeking highly skilled and motivated Generative AI Engineers to join our growing AI team. You will be responsible for designing, developing, and deploying cutting-edge Generative AI solutions using LLMs, Transformers, and Diffusion models. This role involves working on enterprise-grade applications such as intelligent chatbots, document summarization, code assistants, and more. Responsibilities: Design, prototype, and deploy Generative AI models (LLMs, Transformers, Diffusion models) for real-world enterprise use cases. Build and fine-tune LLM-based applications such as: Chatbots, Document Q&A systems, Report generators, Code assistants and Summarization tools. Apply prompt engineering, Retrieval-Augmented Generation (RAG), and context-aware pipelines to enhance model accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores using Python, Java, or Node.js. Collaborate with architects to define scalable, secure, and cost-efficient AI service architectures. Implement AI/ML pipelines for training, validation, and deployment using tools like MLflow, Vertex AI, or Azure ML. Monitor model performance, detect drift, and drive continuous improvement. Optimize inference performance and cost through model compression, quantization, and API optimization. Ensure compliance with AI ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic AI adoption roadmap and reusable AI Workbench/platform components. Mandatory Skills Description: Overall 7+ years of experience and relevant 4+ years of Gen AI. Strong programming skills in Python (preferred), with experience in Java or Node.js. Hands-on experience with LLMs (e.g., GPT, LLa MA, Claude, Mistral), Transformers, and Diffusion models. Experience with Hugging Face Transformers, Lang Chain, LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or related field. .
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.