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Added 6 mins ago
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Added 51 mins ago
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Added 1 hour ago
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Senior Director, Forward Deployed Engineer - Operational AI
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Design, code, and integrate production solutions across Aerospike, enterprise data systems, AI frameworks, and agent orchestration layers.

Accellor

Forward Deployed Engineer

Remote, United States

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Job details

Experience: 0–4 years of relevant experience in software engineering, AI engineering, or applied AI development.
Posting language: English
Posted: Nov 2, 2026

Original job description

Accellor is an AI-native services firm focused on delivering measurable business outcomes through advanced AI, data, and engineering capabilities. We help global innovators and Fortune 100 enterprises modernize their architectures, scale operations, and operationalize intelligence across the enterprise.

Our core focus centers on transforming advanced AI from proofs-of-concept into production-grade systems. Spanning AI solutions, robust data services, enterprise applications, and product engineering - we tailor our offerings to the distinct operational needs of industries such as healthcare, life sciences, telecom, retail, financial services, and technology.

Operating as an extension of our clients' teams, we combine design thinking with technology-agnostic architectures to ensure faster time-to-value and seamless system interoperability. From initial strategy to full-scale deployment, we build intelligent, connected ecosystems that drive sustained enterprise value and redefine the future of transformation. Role Summary

:

We are looking for Forward Deployed 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:

Develop features, resolve defects, refactor applications, and create automated tests using AI-assisted development tools. Use Cursor and other coding environments effectively for repository exploration, implementation, debugging, testing, and code review. Translate product requirements into well-defined engineering tasks, technical approaches, and acceptance criteria. Take ownership of code quality and delivery readiness, including changes produced by AI models or agents. Collaborate with product, engineering, and quality teams to deliver reliable software releases.

Develop and Evaluate Prompts:

Design and maintain effective prompts, repository instructions, context strategies, and reusable workflows for software engineering tasks. Provide models with the relevant architecture, dependencies, coding standards, constraints, and expected outcomes. Establish repeatable evaluations to assess correctness, consistency, requirement coverage, and the need for human intervention. Improve prompts and workflows using evidence from test results, code reviews, and delivery outcomes.

Orchestrate Coding Agents:

Build coordinated groups of coding agents to support planning, implementation, testing, review, and documentation. Design reliable task delegation, handover, takeover, and recovery workflows with clear ownership and shared task state. Manage parallel execution, task dependencies, isolated workspaces, and integration conflicts. Define appropriate tool access, execution boundaries, and human review checkpoints. Ensure agent-produced changes can be traced, reviewed, tested, and integrated into the codebase.

Improve Engineering Efficiency:

Select models and tools based on task requirements, quality, latency, and cost. Optimize context size, token consumption, model routing, retries, and agent execution overhead. Establish baselines and measure improvements in development throughput, delivery time, review effort, and AI expenditure. Maintain clear reporting on weekly and monthly delivery trends, including pull requests raised and merged. Share validated practices and reusable tools that improve the effectiveness of the wider engineering team

Minimum Qualifications:

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. Proficiency in at least one programming language, such as Python, TypeScript/JavaScript, Java, Go, or C#. Hands-on experience applying frontier models, including OpenAI models or Anthropic Claude, to software development. Practical experience with Cursor or comparable AI coding environments. Strong prompt engineering skills, including task decomposition, context preparation, structured outputs, and iterative evaluation. Working knowledge of Git, pull requests, debugging, automated testing, APIs, and CI/CD. Ability to understand existing codebases and independently assess the correctness of AI-generated code. Clear communication, collaborative working habits, and ownership of engineering outcomes.

Preferred Qualifications:

Experience developing workflows with multiple coding agents, including delegation, handover, and integration. Familiarity with model APIs, tool calling, Model Context Protocol (MCP), and agent orchestration frameworks. Experience evaluating prompts and models using defined datasets, acceptance criteria, and automated checks. Experience monitoring AI usage, engineering productivity, and cost efficiency. Exposure to cloud platforms, containers, deployment automation, and application observability. A portfolio of working software demonstrating effective use of AI tools, supported by tests and technical documentation. 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. Engineering excellence -

Expected Contribution:

Produce maintainable, secure, and reliable software that meets product and technical requirements.

Representative Indicators:

Acceptance-criteria completion; automated verification results; review rework; escaped defects; release stability.

AI workflow effectiveness - Expected Contribution:

Develop prompts and workflows that consistently produce useful, verifiable engineering results.

Representative Indicators:

Evaluation success rates; output consistency; first-pass acceptance; human correction and intervention required.

Cost and resource efficiency - Expected Contribution:

Improve the economics of AI-assisted development while maintaining delivery and quality standards.

Representative Indicators:

AI cost per accepted task or merged PR; token consumption; retry overhead; improvement against established baselines.

Agent orchestration - Expected Contribution:

Coordinate coding agents effectively across task execution, handovers, and integration.

Representative Indicators:

Successful task completion and handovers; recovery effectiveness; integration conflicts; manual intervention.

Team contribution - Expected Contribution:

Extend individual impact through reusable tools, clear documentation, and effective collaboration.

Representative Indicators:

Adoption of shared workflows; reuse of validated prompts and tools; improvements in team delivery outcomes. These indicators support a broader assessment of engineering impact. Pull request volume is considered in the context of complexity, business value, collaboration, and software quality.

What you will bring:

Strong technical curiosity, thoughtful judgment, and a commitment to building dependable software. You will be comfortable experimenting with emerging tools, validating their results, and taking responsibility for the code and systems you help deliver.

Pay Transparency:

At Accellor, we believe in equitable compensation. The base salary for this position is $90,000 to $130,000 and there may be an annual bonus. The actual pay will depend on your skills, experience, and qualifications. The salary range is subject to change. Speak with your recruiter about any questions.

Benefits:

We strive to offer benefits that support the diverse needs of our employees. Our package includes perks like flexible and discretionary time off, healthcare coverage for you and your loved ones, and a retirement savings plan to help you plan for the future. Additionally, we offer access to flexible spending and health savings accounts, life and AD&D insurance, and opportunities for professional development. Accellor is proud to be an equal-opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristics.

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