Accellor

Accellor is an AI-native services firm purpose-built for the post-ChatGPT era. Free from legacy constraints, we focus on delivering measurable business outcomes through advanced AI, data, and engineering capabilities. Our mission is to operationalize AI at scale and unlock sustained enterprise value.
Our offerings span AI solutions, data services, enterprise applications, and product engineering, tailored to industry-specific needs across healthcare, life sciences, telecom, retail, financial services, and technology. By leveraging design thinking and technology-agnostic architectures, we ensure faster time-to-value and seamless interoperability.
With a proven track record of enabling Fortune 100 enterprises and global innovators, Accellor stands as a trusted partner for organizations seeking to harness the full potential of AI. Our vision is clear: to build intelligent, connected ecosystems that deliver measurable outcomes and redefine the future of enterprise transformation.
Role Summary:
We are looking for a GenAI Engineer to advance how software is designed, built, tested, and delivered using frontier AI models and coding agents.
You will work with models from OpenAI, Anthropic Claude, and other providers, alongside AI development environments such as Cursor. You will combine strong software engineering fundamentals with prompt engineering and agent orchestration to improve development productivity, software quality, and cost efficiency.
This role is suited to engineers who enjoy building working software, evaluating new approaches, and turning successful experiments into reliable engineering practices. Recent graduates with strong projects and demonstrable skills are encouraged to apply.
Key Responsibilities:
Build and Deliver Software:
<ul>
<li>Develop features, resolve defects, refactor applications, and create automated tests using AI-assisted development tools.</li>
<li>Use Cursor and other coding environments effectively for repository exploration, implementation, debugging, testing, and code review.</li>
<li>Translate product requirements into well-defined engineering tasks, technical approaches, and acceptance criteria.</li>
<li>Take ownership of code quality and delivery readiness, including changes produced by AI models or agents.</li>
<li>Collaborate with product, engineering, and quality teams to deliver reliable software releases.</li>
</ul>
Develop and Evaluate Prompts:
<ul>
<li>Design and maintain effective prompts, repository instructions, context strategies, and reusable workflows for software engineering tasks.</li>
<li>Provide models with the relevant architecture, dependencies, coding standards, constraints, and expected outcomes.</li>
<li>Establish repeatable evaluations to assess correctness, consistency, requirement coverage, and the need for human intervention.</li>
<li>Improve prompts and workflows using evidence from test results, code reviews, and delivery outcomes.</li>
</ul>
Orchestrate Coding Agents:
<ul>
<li>Build coordinated groups of coding agents to support planning, implementation, testing, review, and documentation.</li>
<li>Design reliable task delegation, handover, takeover, and recovery workflows with clear ownership and shared task state.</li>
<li>Manage parallel execution, task dependencies, isolated workspaces, and integration conflicts.</li>
<li>Define appropriate tool access, execution boundaries, and human review checkpoints.</li>
<li>Ensure agent-produced changes can be traced, reviewed, tested, and integrated into the codebase</li>
</ul>
Improve Engineering Efficiency:
<ul>
<li>Select models and tools based on task requirements, quality, latency, and cost.</li>
<li>Optimize context size, token consumption, model routing, retries, and agent execution overhead.</li>
<li>Establish baselines and measure improvements in development throughput, delivery time, review effort, and AI expenditure.</li>
<li>Maintain clear reporting on weekly and monthly delivery trends, including pull requests raised and merged.</li>
<li>Share validated practices and reusable tools that improve the effectiveness of the wider engineering team.</li>
</ul>
Requirements
Minimum Qualifications:
<ul>
<li>0–4 years of relevant experience in software engineering, AI engineering, or applied AI development. Academic, personal, and open-source projects will also be considered.</li>
<li>Proficiency in at least one programming language, such as Python, TypeScript/JavaScript, Java, Go, or C#.</li>
<li>Hands-on experience applying frontier models, including OpenAI models or Anthropic Claude, to software development.</li>
<li>Practical experience with Cursor or comparable AI coding environments.</li>
<li>Strong prompt engineering skills, including task decomposition, context preparation, structured outputs, and iterative evaluation.</li>
<li>Working knowledge of Git, pull requests, debugging, automated testing, APIs, and CI/CD.</li>
<li>Ability to understand existing codebases and independently assess the correctness of AI-generated code.</li>
<li>Clear communication, collaborative working habits, and ownership of engineering outcomes.</li>
</ul>
Preferred Qualifications:
<ul>
<li>Experience developing workflows with multiple coding agents, including delegation, handover, and integration.</li>
<li>Familiarity with model APIs, tool calling, Model Context Protocol (MCP), and agent orchestration frameworks.</li>
<li>Experience evaluating prompts and models using defined datasets, acceptance criteria, and automated checks.</li>
<li>Experience monitoring AI usage, engineering productivity, and cost efficiency.</li>
<li>Exposure to cloud platforms, containers, deployment automation, and application observability.</li>
<li>A portfolio of working software demonstrating effective use of AI tools, supported by tests and technical documentation.</li>
</ul>
Performance and Impact:
Success in this role is demonstrated through sustained contributions to software delivery, engineering quality, and team effectiveness. Performance expectations will reflect the scope, complexity, and level of ownership associated with the role.
Delivery and execution: Expected Contribution: Deliver meaningful, well-scoped changes with predictable execution and clear ownership. Representative Indicators: Weekly and monthly PR submissions and merges; task completion; time to review-ready code; PR cycle time.

Gen AI Engineers

Gen AI Engineers

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