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Job Summary We are hiring an Agentic AI Engineer to build and ship production AI systems as part of BayOnes AI Strategy and Innovation Office. The role sits within a team-oriented, distributed engineering team and contributes to both internal projects and solutions developed for the practices client portfolio. Our portfolio extends across many industries and business areas, and the work shifts across technical areas from one engagement to the next. The role requires adaptability, breadth of thinking, and the discipline to bring rigorous engineering to every deliverable. The Agentic AI Engineer is personally accountable for the quality and reliability of every deliverable produced, on both internal and client engagements. The work spans agent-based systems, web applications, data engineering, and infrastructure, with agentic pair programming as the primary working mode. Engineering discipline, sound technical judgment, and adherence to the teams quality standards are expected on every piece of work. The ideal candidate is currently working with agentic AI systems in a hands-on capacity and has a track record of shipping production systems. Professional experience extends beyond generative AI alone, with a broader engineering foundation that demonstrates range across problem types. The role calls for an engineer who delivers consistently, owns what they ship, and brings the same rigor to unfamiliar problems as to familiar ones. Responsibilities - Agentic AI Development (45%): Build AI systems for internal and client engagements, from initial POC through production delivery. - Agentic AI Development (45%): Design and implement agent-based solutions for complex, multi-step problems across the practices diverse engagement portfolio. - Agentic AI Development (45%): Build POCs and demos that demonstrate technical feasibility and value to stakeholders. - Agentic AI Development (45%): Evaluate and select the right approach for each problem, including knowing when an agent-based approach is appropriate and when a simpler method fits. - Agentic AI Development (45%): Participate in system design within the team, contributing architectural thinking and implementation expertise. - Agentic AI Development (45%): Own the full lifecycle of systems built: design, implementation, evaluation, and reliability. - Engineering and Development (25%): Build and maintain web applications, APIs, and backend services for internal and client engagements. - Engineering and Development (25%): Design and work with database schemas, write and optimize queries, and manage data across relational and vector database systems. - Engineering and Development (25%): Build data pipelines and integrations across the practices data platforms. - Engineering and Development (25%): Contribute to infrastructure work, including containerized deployments and cloud configuration. - Engineering and Development (25%): Move between technical areas with engineering discipline, applying the same rigor regardless of the specific technology or context. - Engineering and Development (25%): Deliver work that meets the teams standards across both internal initiatives and solutions developed for the practices client portfolio. - Quality and Delivery Excellence (20%): Write and maintain tests (unit, integration, end-to-end) as part of every deliverable. - Quality and Delivery Excellence (20%): Participate in code reviews with substantive technical feedback, both giving and receiving. - Quality and Delivery Excellence (20%): Maintain documentation for systems built, including architecture decisions, setup instructions, and integration points. - Quality and Delivery Excellence (20%): Apply evaluation discipline to agent systems, including structured evaluation harnesses and observability for deployed systems. - Quality and Delivery Excellence (20%): Meet delivery commitments on time and communicate accurately on progress and blockers. - Quality and Delivery Excellence (20%): Uphold the teams code quality standards, testing practices, and documentation requirements across all work. - Growth and Collaboration (10%): Stay current with developments in the agentic AI landscape, language-model orchestration, and the broader AI engineering field. - Growth and Collaboration (10%): Learn new tools, frameworks, and patterns as the practice adopts them, working from reference implementations and team guidance. - Growth and Collaboration (10%): Actively expand technical skills and depth across the practice's engineering domains, pursuing breadth as the engagement portfolio evolves. - Growth and Collaboration (10%): Contribute to team knowledge by sharing findings from new tools, techniques, and engagement experiences. - Growth and Collaboration (10%): Incorporate feedback from code reviews, system evaluations, and team retrospectives into ongoing work. - Growth and Collaboration (10%): Collaborate effectively within the distributed team, including responsive communication and .
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.