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LEXTECH is a Thai AI technology company at the intersection of Customer Experience and human interaction — specializing in Natural Language Processing and Computer Vision. As part of OSD Group, one of Thailand’s leading infrastructure and technology groups, we build products that help businesses understand, engage, and respond to their customers at scale. Responsibilities Technical Leadership & Research • Stay ahead of the AI/ML landscape and translate emerging developments into actionable decisions for the team — particularly across Large Language Models, computer vision, and applied NLP. The field moves fast. “We’ll look into it later” is how you become irrelevant. • Evaluate and benchmark new technologies against current implementations. Own the recommendation on whether to adopt, adapt, or hold. Not everything that trends on Papers With Code belongs in production. • Define the technical direction for AI/ML initiatives, balancing innovation with production stability across a mix of production models and LLM-powered applications. Hands-On Implementation • Architect and lead the development of ML solutions end-to-end — from problem framing and model selection through to deployment and monitoring. • Apply pragmatic judgment on when to fine-tune foundation models, when to use RAG or prompt-based approaches, and when classical ML still wins. No ideology, just results. • Own optimization decisions: model performance, inference efficiency, and resource utilization. Accuracy without scalability is a prototype, not a product. Team Management • Lead and grow a small team of ML Engineers — including workload planning, technical mentorship, and performance development. • Set clear standards for code quality, experimentation rigor, and model lifecycle management across the team. • Act as the bridge between technical execution and business stakeholders — translating requirements into scoped ML work and communicating results without the jargon tax. Qualifications Required • Degree in Computer Science, Computer Engineering, Applied Mathematics, or equivalent field. What you’ve built matters more than where you studied. • Proven hands-on experience delivering end-to-end ML solutions in production environments — not just research or notebooks. • At least 3 years of experience in AI/ML disciplines. • Currently hold a position of Senior-level at minimum. We are not looking for entry level position. • Demonstrated experience leading or mentoring a technical team, even informally. • Strong command of Python and comfort working in Linux-based environments. Technical Depth • Strong fundamentals in classical ML and deep learning — you should know why things work, not just that they work. Hands-on experience with PyTorch preferred. • Fluent in the modern LLM stack: fine-tuning, prompt engineering, RAG, and agentic patterns. Familiarity with LangChain, LlamaIndex, or equivalent is a plus. • Applied experience across both Computer Vision (detection, classification, segmentation, tracking) and NLP (transformer architectures, embedding models, semantic search). • MLOps sensibility: model versioning, monitoring, and retraining discipline. 10+ models and a growing fleet of LLM-powered apps don’t babysit themselves either. Soft Skills & Mindset • Genuine curiosity about human behavior as expressed through language and imagery — this isn’t decorative, it’s core to what we build. • Strong English reading and writing proficiency — most of the literature, tooling, and collaboration happens in English. • Comfortable with ambiguity. At this level, the problems are rarely well-defined before you arrive.
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.
