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Job Description Role responsibilities - Develop and optimize LLM models using state-of-the-art techniques, focusing on RAG (Retrieval-Augmented Generation). - Implement content extraction methods to enhance the accuracy and relevance of generated responses. - Collaborate with cross-functional teams to identify and address LLM development and deployment challenges. - Stay updated with the latest advancements in natural language processing (NLP) and machine learning research. - Provide technical guidance and mentorship to junior team members. - Contribute to documenting and disseminating best practices in LLM development and deployment. - Implement RPA solutions to automate repetitive tasks and improve operational efficiency. - Collaborate with data scientists and business analysts to understand data requirements and translate them into technical solutions. - Monitor performance, troubleshoot issues, and optimize data workflows for scalability and reliability - Stay up-to-date with the latest trends and technologies in data engineering, RPA, and AI/ML Required Skill Sets: - Bachelors or higher degree in Computer Science, Engineering, or a related field. - Proven experience in developing and fine-tuning LLM models, preferably with a focus on RAG. - Hands-on experience with RPA tools such as UiPath, Automation Anywhere, Blue Prism, Power Automate - Strong proficiency in Python programming and familiarity with relevant libraries/frameworks such as Tensor Flow, PyTorch, scikit-learn. - Solid understanding of natural language processing (NLP) fundamentals and machine learning principles. - Experience with content extraction techniques such as Named Entity Recognition (NER), text summarization, and information retrieval. - Familiarity with the concept of local LLMs and their applications in specific domains or languages. - Excellent problem-solving skills and the ability to work effectively in a cooperative team environment. - Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders Preferred Qualifications: - Experience with cloud platforms such as AWS, Azure, or Google Cloud - Prior working knowledge in implementing RPA and AI/ ML Methods. - Certification in RPA or AI/ML technologies. - Contributions to open-source projects related to natural language processing or machine learning. - Knowledge of big data technologies (e.g., Hadoop, Spark, Kafka) .
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.