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You will be joining Momentum91 as an AI Engineer with a focus on researching, designing, and integrating AI-based solutions within digital products. Your primary responsibilities will include:- Researching and evaluating AI/ML technologies, frameworks, and APIs for various business use cases.- Designing and developing prototypes or POCs to validate AI-driven features and workflows.- Implementing and integrating AI models into production environments in collaboration with engineering teams.- Identifying opportunities for automation, prediction, recommendation, and personalization within existing modules.- Working with structured and unstructured data to train, fine-tune, and optimize models.- Collaborating with product managers, designers, and engineers to align AI initiatives with the overall product vision.- Staying updated with the latest trends in AI/ML, LLMs, and generative AI tools.- Evaluating and integrating third-party AI tools or APIs.- Defining metrics for evaluating model performance, user impact, and ROI of AI Documenting AI system design, implementation approach, and model lifecycle management.As an AI Engineer at Momentum91, you should possess the following technical requirements:- Strong programming skills in Python and experience with frameworks like TensorFlow, PyTorch, or Scikit-learn.- Understanding of modern NLP, computer vision, and recommendation system techniques.- Experience with integrating AI services via APIs or SDKs into full-stack Knowledge of cloud-based AI tools and services like AWS SageMaker, Azure AI, Google Vertex AI.- Familiarity with vector databases, embeddings, and retrieval-augmented generation methods.- Proficiency in working with REST APIs and microservice architectures.- Strong understanding of data preprocessing, feature engineering, and model deployment pipelines.- Experience with prompt engineering and fine-tuning large language models (LLMs) is a plus.- Understanding of MLOps concepts and version control for model lifecycle management.In terms of qualifications, you are required to have:- A Bachelors degree in Computer Science, Artificial Intelligence, or a related technical field.- 2 to 4 years of hands-on experience in building or integrating AI-based solutions into products.- Proven ability to translate product requirements into actionable AI implementation plans.- Experience in PropTech, CRM, or enterprise SaaS domains will be beneficial.- Excellent communication and collaboration skills to work effectively with multidisciplinary teams.- A Masters degree or certifications in AI/ML (AWS, Azure, or Google Cloud AI Engineer) would be an added advantage. You will be joining Momentum91 as an AI Engineer with a focus on researching, designing, and integrating AI-based solutions within digital products. Your primary responsibilities will include:- Researching and evaluating AI/ML technologies, frameworks, and APIs for various business use cases.- Designing and developing prototypes or POCs to validate AI-driven features and workflows.- Implementing and integrating AI models into production environments in collaboration with engineering teams.- Identifying opportunities for automation, prediction, recommendation, and personalization within existing modules.- Working with structured and unstructured data to train, fine-tune, and optimize models.- Collaborating with product managers, designers, and engineers to align AI initiatives with the overall product vision.- Staying updated with the latest trends in AI/ML, LLMs, and generative AI tools.- Evaluating and integrating third-party AI tools or APIs.- Defining metrics for evaluating model performance, user impact, and ROI of AI Documenting AI system design, implementation approach, and model lifecycle management.As an AI Engineer at Momentum91, you should possess the following technical requirements:- Strong programming skills in Python and experience with frameworks like TensorFlow, PyTorch, or Scikit-learn.- Understanding of contemporary NLP, computer vision, and recommendation system techniques.- Experience with integrating AI services via APIs or SDKs into full-stack Knowledge of cloud-based AI tools and services like AWS SageMaker, Azure AI, Google Vertex AI.- Familiarity with vector databases, embeddings, and retrieval-augmented generation methods.- Proficiency in working with REST APIs and microservice architectures.- Strong understanding of data preprocessing, feature engineering, and model deployment pipelines.- Experience with prompt engineering and fine-tuning large language models (LLMs) is a plus.- Understanding of MLOps concepts and version control for model lifecycle management.In terms of qualifications, you are required to have:- A Bachelors degree in Computer Science, Artificial Intelligence, or a related technical field.- 2 to 4 years of hands-on experience in building or integrating AI-base .
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.