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Duties & Responsibilities Translate business requirements into scalable and well-documented ML pipelines and AI solutions using Databricks, Azure AI, and Snowflake. Architect and implement scalable GenAI solutions using Azure/ GCP AI, Databricks, and Snowflake Develop and deploy agentic workflows using LangChain, LangGraph, and OpenAI Agents SDK for autonomous task execution. Lead experimentation and fine-tuning of LLMs (e.g., GPT-4, Claude, LLaMA 2) for enterprise use cases such as summarization, personalization, and content generation. Integrate GenAI models into business applications with Human-in-the-Loop (HITL) validation and feedback loops. Build and maintain MLOps/LLMOps pipelines using MLflow, ONNX, and Unity Catalog for reproducibility and governance. Monitor model performance and ensure responsible AI operations through observability tools like OpenTelemetry and Databricks AI Gateway. Stay current with GenAI and LLM advancements, including frameworks like LangChain, LlamaIndex, and Gemini, and apply them to enterprise use cases. RequirementsBasic Qualifications Bachelors or Masters degree in Computer Science, Engineering, or related field. 58 years of experience in AI/ML engineering, with at least 2 years focused on GenAI and LLMs. Proven experience deploying agentic AI systems in production environments. Strong understanding of NLP, deep learning, and multi-modal AI (text, image, audio). Experience with enterprise-grade AI governance and security practices. Preferred Qualifications Languages: Python, SQL, PySpark Agent frameworks: LangChain, LangGraph, Hugging Face, OpenAI SDK, Gemini GenAI Tools: Azure AI Foundry, Vertex AI, Databricks AI MLOps/LLMOps: MLflow, ONNX, Unity Catalog Data Platforms: Databricks, Snowflake, Data Lake, Knowledge graphs Understanding of AI governance, including model explainability, fairness, and security (e.g., prompt injection, data leakage mitigation). .
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.