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About CONXAI CONXAI has built a no-code, agentic AI platform for the Architecture, Engineering and Construction (AEC) and physical industries, focused on knowledge-automation. We automate high-stakes, knowledge-intensive workflows traditionally trapped in siloed data, fragmented tools and tacit (undocumented) human expertise. Our multi-agent systems perform complex reasoning in the physical world; and transform bespoke, service-heavy processes into scalable Service-as-a-Software automation. CONXAI is trusted by some of the leading AEC companies in Europe, US, LATAM and Japan. Your Role You bridge the gap between SOTA research and real-world deployments. You are responsible for ensuring Computer Vision models perform reliably when exposed to complex, unstructured customer data. Core Responsibilities - Own the Feedback Loop: Monitor production data to identify exactly where and why models struggle in specific customer environments - Diagnose & Propose: Analyze discrepancies between model output and reality to propose concrete algorithmic or data-driven fixes - Continuous Validation: Own the \"last-mile delivery\" by ensuring proper use-case setup and validating the accuracy of final results for the customer - Drive Data-Centric Improvements: Lead the data \"flywheel\" by curating specialized datasets and integrating high-value customer data for model retraining - Operationalize SOTA: Adapt high-level architectures into performant, cost-effective solutions tailored for specific customer use-cases - Validate for Impact: Design evaluation frameworks that measure true customer value rather than relying solely on standard benchmarks What Were Looking For - Bachelor's / Master's degree in Computer Science (or related) or Civil Engineering with specialization in Data Science / Lean Construction - Experience with training, and evaluating Deep Learning models in PyTorch - Valuable understanding of basics in machine learning and computer vision, specifically representation learning - Experience training and fine-tuning a Deep Learning architecture for: - Object Detection - Segmentation - Large Vision Language Models - Experience with data curation, visualization, outlier detection, active learning and hard-negative mining - Proficient in Python and good software engineering skills - Ability to work in a team-oriented environment - Strong problem-solving skills - Good communication and interpersonal skills (fluent in English) Why CONXAI - Edge of Innovation: Be at the absolute forefront of AI in the construction tech space - High Autonomy: Contribute to a new paradigm for multi-modal scene understanding and reasoning - owning the logic, performance, and customer impact - Top-Tier Peer Group: Work with a global team of ML engineers, software engineers and industry practitioners - Equity & Scale: Competitive compensation with significant equity upside .
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.