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Responsibilities: Design, develop, and implement end-to-end Generative AI solutions using Microsoft Copilot and Amazon Bedrock, focusing on practical, commercially viable use cases. Collaborate with product owners, data scientists, and other engineers to define requirements, design model architectures, and integrate AI models into existing systems and workflows. Utilize and fine-tune pre-trained models available through Microsoft Copilot and Amazon Bedrock, as well as other relevant foundation models, for specific business needs. Develop and maintain robust data pipelines for training, fine-tuning, and deploying AI models. Implement best practices for model evaluation, monitoring, and optimization in a production environment. Ensure the scalability, reliability, and performance of deployed AI applications. Stay up-to-date with the latest advancements in Generative AI, Microsoft Copilot, Amazon Bedrock, and related technologies. Troubleshoot and resolve issues related to model performance, deployment, and integration. Contribute to the development of our internal AI/ML MLOps practices and standards. Communicate technical concepts and results effectively to both technical and non-technical stakeholders. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. Proven experience (3+ years) in developing and deploying AI/ML models in a commercial environment. Hands-on experience with Generative AI concepts, models (e.g., LLMs, transformers), and their applications. Demonstrated experience in building and implementing solutions using Microsoft Copilot and/or Amazon Bedrock. Proficiency in programming languages commonly used in AI development (e.g., Python). Experience with cloud platforms (Azure and/or AWS) and their AI/ML services. Solid understanding of the MLOps lifecycle and experience deploying models to production. Experience with API development and integration. Strong problem-solving skills and the ability to work independently and as part of a team. Excellent communication and interpersonal skills. Familiarity with other Generative AI frameworks and libraries (e.g., Hugging Face, TensorFlow, PyTorch) would be an advantage. Interested parties are invited to apply by clicking " Quick Apply " and submitting a full resume, including current and expected salary details. SmarTone is an equal opportunity employer. All personal data provided will be treated in strict confidence and used solely for recruitment-related purposes. Such data may be transferred to our agents, contractors, or third-party service providers for processing in connection with your application. Only shortlisted candidates will be contacted. For more information about SmarTone, please visit our website at www.smartone.com.
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.