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Key Responsibilities : - Design, develop, and deploy AI-driven applications and intelligent software solutions. - Build and optimize machine learning and deep learning models for production environments. - Develop applications leveraging Large Language Models (LLMs), Generative AI, and Natural Language Processing (NLP). - Fine-tune foundation models using domain-specific datasets and implement Retrieval-Augmented Generation (RAG) pipelines. - Develop AI services and RESTful APIs for seamless integration with enterprise applications. - Build scalable backend systems to support AI inference and model serving. - Design data pipelines for model training, evaluation, and continuous improvement. - Implement prompt engineering techniques to improve LLM performance and response quality. - Optimize model accuracy, latency, scalability, and infrastructure utilization. - Collaborate with software engineers, product managers, and data scientists throughout the product lifecycle. - Deploy AI models using containerization and orchestration technologies. - Implement MLOps practices for automated model training, deployment, monitoring, and version control. - Evaluate emerging AI technologies and recommend suitable solutions for business use cases. - Ensure AI applications meet security, governance, and responsible AI standards. - Troubleshoot production issues and continuously improve system performance. Required Skills : - Strong programming skills in Python. - Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn. - Hands-on experience with Large Language Models (LLMs) and Generative AI technologies. - Strong understanding of NLP, Transformers, Embeddings, and Vector Databases. - Experience building RAG-based applications. - Knowledge of prompt engineering and LLM optimization techniques. - Experience with LangChain, LangGraph, LlamaIndex, or similar AI orchestration frameworks. - Experience developing REST APIs using FastAPI or Flask. - Strong understanding of data structures, algorithms, and software engineering principles. - Experience with SQL and NoSQL databases. - Familiarity with Docker, Kubernetes, and containerized deployments. - Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform. - Knowledge of CI/CD pipelines and MLOps tools. - Familiarity with Git and Agile development methodologies. Preferred Qualifications : - Experience working with OpenAI, Anthropic, Google Gemini, or open-source LLMs such as Llama, Mistral, or Qwen. - Exposure to AI agents, multi-agent systems, and autonomous workflows. - Experience with vector databases such as Pinecone, Weaviate, Milvus, or ChromaDB. - Knowledge of distributed computing, GPU optimization, and model serving frameworks. - Experience with monitoring and observability tools for AI applications. - Understanding of responsible AI, model governance, and AI security best practices. Soft Skills : - Robust analytical and problem-solving abilities. - Excellent communication and collaboration skills. - Ability to work independently and in cross-functional teams. - Passion for innovation and continuous learning in AI technologies. - Robust ownership, accountability, and attention to detail. - Ability to thrive in a fast-paced product development environment. .
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.