[Hiring] MCP Expert @AquSag Technologies

Role Description

In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required; your domain knowledge is what matters.

• Create Reinforcement Learning Environments to test an AI model’s ability to solve complex software engineering problems using Model Context Protocol (MCP) tools.
• Tasks may involve:
  • Fixing bugs
  • Implementing features
  • Refactoring code
  • Optimizing performance
• Design reproducible environments, deterministic verification, and golden reference solutions that accurately measure both MCP tool use and software engineering ability.

Qualifications

• Proficiency in C++, Python, JAVA, GoLang, Typescript, or Rust.
• Deep understanding of algorithms, data structures, and performance tuning.
• Demonstrated experience in debugging complex software issues and delivering maintainable solutions.
• Strong background in feature development and codebase refactoring.
• Proven ability to optimize software for performance and scalability.
• Exceptional written and verbal communication skills, with a keen attention to detail.
• Track record of success in collaborative, cross-functional teams, ideally in remote settings.

Requirements

• 3+ years of experience.
• Full-time (Contractual / Freelancing) position.
• Remote location.

Preferred Qualifications

• Previous experience working on large-scale, distributed codebases.
• Familiarity with modern AI or machine learning systems is a plus, though not required.
• Background in participating in rigorous code reviews and contributing to the development of software best practices.

Company Description

AquSag supports engineering and AI workflow delivery across GenAI programs, benchmark evaluation, enterprise engineering, and distributed technical operations.

• Our work spans RLHF and post-training workflows, benchmark and SWEbench-style evaluation programs, GenAI engineering, MLOps support, coding evaluation, multilingual AI operations, red teaming, and QA-heavy technical review environments.
• We support engineering teams across Python, Java, .NET, SAP, Salesforce, cloud, DevOps, QA, and enterprise platform environments.
• A large part of our work involves helping companies scale technical programs quickly without losing delivery consistency, review quality, or operational reliability.
• We work across AI platforms, product companies, enterprise technology teams, and global delivery environments where engineering and evaluation workflows increasingly operate side by side.

• Key areas:
  • RLHF & AI evaluation
  • Benchmark & SWEbench-style workflows
  • GenAI engineering & MLOps
  • Red teaming & adversarial testing
  • Enterprise engineering teams
  • Cloud, DevOps & QA
  • Multilingual & domain-specific AI programs