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Key Responsibilities 1. AI Model Evaluation 20% "Buy vs. Build" Technical Evaluation: Establish a rigorous engineering decision matrix to determine when to utilize commercial APIs versus fine-tuning open-source models on internal infrastructure. 2. Engineering Leadership & Solution Delivery 70% Enterprise-Scale Delivery: Oversee the full lifecycle of AI products from proof-of-concept (PoC) to production. Ensure solutions meet rigorous IT standards for scalability, reliability, and security. MLOps & Financial Stewardship (FinOps): Implement CI/CD pipelines for model retraining and deployment. Architectural Review: Review and approve technical designs, ensuring the seamless integration of AI agents with legacy systems, Vector Databases, and Enterprise Data Warehouses. Technical Mentorship: Act as the " Technical Mentor" for internal squads. Guide business teams through the use case development and prototyping phase, helping them convert raw ideas into deployable. 3. AI Governance 10% AI Guardrails & Compliance: Collaborate with Legal, Risk, and Data Privacy officers to enforce an AI Governance Framework. Ensure strict adherence to PDPA/GDPR, data residency requirements, and IP protection standards. Qualifications & Skills Bachelor’s degree in computer science, Engineering, AI/ML, or a related field. 3-5 years of technical experience in AI/ML Engineering experience. Proven track record of developing, testing and deploying Generative AI / LLM solutions. Solid understanding of MLOps , CI/CD for ML, and Data pipelines. Experience in Cloud AI and Infrastructure management. Experience in API integration with Generative AI, prompt engineering, RAP Pipeline design and fine-tuning basic. Strong Communication Proficiency in English and Thai. Self-Directed Learning Velocity Intellectual Curiosity & Experimentation Mindset Resilience to Technical Obsolescence
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.
