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Key Responsibilities Design, build, and optimize scalable data pipelines for AI/ML applications. Develop, train, evaluate, and deploy Machine Learning and Deep Learning models. Build production-ready LLM applications using Retrieval-Augmented Generation (RAG), prompt engineering, and vector databases. Fine-tune open-source and foundation models using domain-specific datasets. Develop and maintain end-to-end MLOps pipelines for model deployment, monitoring, and lifecycle management. Perform data preprocessing, feature engineering, exploratory data analysis (EDA), and model evaluation. Develop APIs and AI services for production deployment. Collaborate with cross-functional teams to deliver scalable AI-driven solutions. Monitor model performance, troubleshoot production issues, and maintain technical documentation. Required Skills Mandatory 13 years of experience in Data Science, Data Engineering, or AI/ML development. Strong programming skills in Python and SQL. Hands-on experience with Machine Learning frameworks such as PyTorch, TensorFlow, or Scikit-learn. Experience building LLM-powered applications using RAG, Prompt Engineering, and Embeddings. Hands-on experience with LangChain, LlamaIndex, CrewAI, or n8n for LLM orchestration and AI workflow automation. Experience in LLM fine-tuning and working with Hugging Face models. Knowledge of MLOps concepts including model deployment, monitoring, versioning, and CI/CD. Experience with Git, REST APIs, Linux environments, and data processing libraries. Preferred Experience with vector databases such as Pinecone, Chroma, Milvus, or Weaviate. Familiarity with Docker, Kubernetes, and MLflow. Exposure to Apache Spark or Airflow for data engineering workflows. Experience with cloud platforms (AWS, Azure, or GCP). Primary Technology Stack Languages & Data Processing: Python, SQL, Pandas, NumPy, Apache Spark AI & Machine Learning: PyTorch, TensorFlow, Scikit-learn Application Frameworks: LangChain, LlamaIndex, CrewAI, n8n Core Methodologies: Retrieval-Augmented Generation (RAG), Model Fine-Tuning, Prompt Engineering, Embeddings Models & Infrastructure: OpenAI APIs, Hugging Face Ecosystem, Embedding Models Vector Databases: Pinecone, Chroma, Milvus, Weaviate Databases: PostgreSQL, MongoDB MLOps & DevOps: Docker, Kubernetes, MLflow, CI/CD, Git Cloud Platforms: AWS, Azure, GCP Experience: 13 Years Domain: Data Science | Data Engineering | Machine Learning | Generative AI | MLOps .
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.