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About the JobThis position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI/ML Software Engineer based in United States. This role sits at the intersection of AI engineering, data science, and full-stack software development. You will design, build, and evolve production-grade AI systems, including agentic workflows and autonomous solutions. Working across the technology stack, you will develop robust backend services, intuitive interfaces, and data pipelines that bring AI capabilities into real-world applications. You will contribute to multi-agent orchestration, prompt engineering, model evaluation, and production optimization. The role combines hands-on software development with experimentation and applied research to solve complex technical and business challenges. You will collaborate with engineers, customers, and cross-functional stakeholders in an agile environment where ambiguity and innovation are part of the work. Your contributions will directly support the development of advanced technologies with meaningful applications in the future of flight. Accountabilities AI and agentic systems: Design, build, and maintain agentic workflows, autonomous systems, and multi-agent architectures that automate complex tasks and enhance product capabilities. Production AI development: Integrate AI and machine learning models into scalable, reliable production applications, going beyond model training to address prompts, validation, deployment, and operational performance. Full-stack engineering: Develop clean, maintainable software across the technology stack, including backend services, user-facing interfaces, APIs, and supporting application components. AI orchestration: Implement orchestration logic for multi-agent systems and task automation using appropriate frameworks and architectural patterns. Data pipelines and evaluation: Build prototype-level data pipelines supporting model training, evaluation, review, and data movement between AI components and applications. Model performance and risk management: Monitor and optimize AI systems in production while identifying and mitigating issues such as hallucinations, bias, reliability, and performance degradation. Cross-functional collaboration: Partner with engineers, customers, and business stakeholders to understand requirements, translate needs into technical solutions, and provide effective technical support. Technical innovation: Research and apply emerging developments in AI, LLMs, prompt engineering, and agentic architectures to improve products and user experiences. Agile delivery: Contribute throughout the software development lifecycle, from experimentation and prototyping through implementation, deployment, and continuous improvement. Requirements Education: Bachelor’s degree in Computer Science or a STEM-related discipline. Experience: At least 3 years of professional experience in AI/ML engineering. AI/ML expertise: Strong understanding of AI/ML concepts, LLMs, prompt engineering, and practical AI application development. Software engineering: Proven full-stack engineering experience with strong programming skills, particularly in Python, along with tools such as JupyterHub, GitHub, and Visual Studio. Agentic AI: Experience designing or building agentic workflows, autonomous systems, or multi-agent architectures. AI frameworks: Familiarity with orchestration frameworks such as LangChain, AutoGen, CrewAI, or comparable technologies. Cloud and infrastructure: Experience with cloud platforms such as AWS, Azure, or GCP, as well as containerization technologies including Docker and Kubernetes. Solution architecture: Ability to contribute to scalable solution architecture and integrate AI capabilities into production-grade software systems. Data and model evaluation: Experience creating data pipelines and validating AI model performance through data analysis and structured evaluation. Problem solving: Ability to navigate ambiguous, complex challenges and apply AI/ML techniques to practical, real-world problems. Customer orientation: Ability to interact effectively with customers, understand their needs, and provide appropriate technical guidance and support. Professional profile: A valid LinkedIn profile is requested. Work authorization: Candidates must be legally authorized to work in the United States without current or future employment visa sponsorship. Because the role involves access to U.S. export-controlled information, employment requires the ability to demonstrate qualifying U.S. Person status. Benefits Competitive salary: Base pay range of $95,000–$140,000 USD per year, depending on experience, education, skills, and other relevant factors. Performance incentive: Eligibility for an annual discretionary bonus based on the applicable compensation plan. Remote flexibility: Remote position in the United States. Healthcare: Medical, dental, vision, and prescription drug coverage. Wellbeing resources: Access to a health coach and a confidential Employee Assistance Program providing 24/7 assessment, counseling, and referral services. Retirement: 401(k) savings plan with company matching and retirement contributions, plus access to financial planning resources. Additional support: Tuition assistance, adoption assistance, paid parental leave, disability insurance, and life insurance. Paid time off: Vacation and paid time off for illness. Professional development: Opportunities to work on challenging AI/ML initiatives, develop technical expertise, and contribute to innovative products and technologies. Relocation: No relocation assistance is provided. Skills: production ai,multi-agent orchestration,agentic systems,full-stack development,ai/ml engineering
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.