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About Us Founded in 2011, Modus is a global, fully remote team of world-class technologists who thrive in a collaborative, innovative environment. We’re a digital product engineering partner for forward-thinking businesses. Our global teams work side-by-side with clients to design, build, and scale custom solutions that achieve real results and lasting change, partnering with industry leaders including AWS, GitHub, and Atlassian. We were fully remote before it was cool! Recognized as one of the Inc. 5000 Fastest Growing Private Companies for nine years and a top remote work company by FlexJobs, we have helped some of the world’s largest brands deliver powerful digital experiences. As an award-winning Atlassian partner with a world-class team, we help organizations innovate and solve complex challenges for Fortune 500 companies and beyond, we want to hear from you. Opportunity We are looking for a Forward Deployed AI Engineer to work closely with clients to identify, prototype, and deploy AI-powered solutions to real-world business challenges. This is a highly hands-on, customer-facing role combining AI engineering, software development, solution architecture, and consulting. You will work directly with users and stakeholders to understand their needs, rapidly build solutions, and iterate based on real-world feedback. The ideal candidate is a strong engineer who is comfortable moving between customer conversations, architecture, coding, prototyping, and production delivery. Key Responsibilities Work directly with clients to understand business challenges, workflows, data, systems, and technical requirements. Translate ambiguous problems into practical AI and software solutions. Design, build, and deploy AI-powered applications, agents, copilots, and intelligent workflows. Integrate LLMs, Generative AI, APIs, enterprise systems, and data sources. Rapidly prototype solutions and validate them with users. Develop supporting APIs, services, data pipelines, and application infrastructure. Design scalable, secure, and maintainable technical architectures. Implement evaluation, monitoring, observability, and feedback mechanisms for AI solutions. Collaborate with product, data, engineering, and business teams to move successful prototypes into production. Communicate technical concepts and recommendations clearly to both technical and non-technical stakeholders. Required Skills Python — Strong software engineering and application development experience. Application Engineering — Experience building, testing, and deploying production applications. AI/ML Integration — Experience integrating AI/ML capabilities into applications and workflows. APIs & System Integration — Experience with APIs, enterprise systems, third-party platforms, and data sources. LLMs / Generative AI — Hands-on experience building applications using LLMs and Generative AI. Cloud Platforms — Experience with AWS, Azure, and/or GCP. TypeScript — Experience developing modern applications or services. Agent Orchestration — Experience or understanding of AI agents, tool calling, agentic workflows, or orchestration frameworks. Customer-Facing / Consulting — Ability to work directly with clients and translate business problems into technical solutions. Solution Architecture — Ability to design end-to-end solutions that are scalable, secure, and aligned to business objectives. Experience & Qualifications Typically 5+ years' experience in software engineering, AI/ML engineering, or a related technical discipline. Experience building and deploying AI/ML or Generative AI applications. Strong problem-solving skills and comfort working with incomplete or evolving requirements. Ability to rapidly prototype and turn ideas into working software. Strong communication and stakeholder management skills. A product-oriented mindset focused on solving the user's problem rather than simply delivering requirements. Ability to work independently within complex enterprise environments. Nice to Have Experience with RAG, vector databases, embeddings, and semantic search. Consulting, systems integration, or professional services experience Experience with AI agent frameworks and orchestration platforms. Experience with Docker, Kubernetes, CI/CD, or Infrastructure as Code. Experience taking AI/ML prototypes through to production. Experience working with large or complex enterprise customers. Experience working in regulated or highly controlled environments. What Success Looks Like You will be successful when you can take an ambiguous customer problem and turn it into a working AI capability that people actually use. The role requires someone who can move quickly between: Customer problem → solution design → architecture → code → prototype → user feedback → iteration → production. By joining our team, you’ll be part of a winning squad that plays to each other’s strengths and celebrates every success together.
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.
