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About The Role Develop and deploy machine learning and AI models for real-world product use cases. Build and improve LLM-powered applications, including AI assistants and conversational systems. Develop RAG (Retrieval-Augmented Generation) pipelines using property, user, and business data. Work on semantic search, recommendation systems, and intelligent property matching. Develop lead scoring and leadagent/property matching models. Process, clean, transform, and analyze large datasets for model development. Design and integrate embeddings, vector databases, and similarity-search systems. Build and maintain AI/ML APIs and integrate models with backend services. Evaluate model performance using appropriate metrics and continuously improve accuracy. Experiment with different ML/LLM models, prompting techniques, fine-tuning approaches, and AI frameworks. Work with engineering and product teams to convert business requirements into AI solutions. Monitor AI systems in production and troubleshoot issues related to performance, latency, and accuracy. Maintain documentation for models, datasets, experiments, and AI pipelines. Stay updated with developments in Generative AI, LLMs, NLP, and machine learning. What we're looking for Bachelor's degree in Computer Science, AI/ML, Data Science, Engineering, or a related field. 13 years of experience in AI/ML engineering or a closely related role. Strong programming skills in Python. Strong understanding of: Machine Learning Deep Learning NLP Model evaluation and optimization Data preprocessing and feature engineering Hands-on experience with ML libraries such as Scikit-learn, PyTorch, or TensorFlow. Practical experience working with LLMs and Generative AI. Experience with OpenAI, Gemini, Claude, Hugging Face, or similar models. Understanding of prompt engineering and LLM evaluation. Experience building RAG-based applications. Familiarity with embeddings and vector databases such as Pinecone, Qdrant, Weaviate, FAISS, or Chroma. Experience working with REST APIs and model deployment. Good understanding of SQL and databases. Familiarity with Git/GitHub and software development workflows. Strong problem-solving and analytical skills. .
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.