Middle AI / ML Engineer G

Join Akvelon — build products used by millions!

Akvelon is an IT company with 20+ years of experience and 1,200+ engineers across 15+ locations worldwide.
We work with both well-known global tech companies, including Microsoft, Facebook, Airbnb, Dropbox, and Pinterest, and with growing startups.
Our teams are involved in different types of engineering projects, from cloud solutions and AI/ML systems to big data, web, and mobile applications.
Since we are remote-first, our engineers work in distributed teams with flexible hours. We value ownership, clear communication, and the ability to take responsibility for your part of the work.

About the role

We are looking for a Middle AI Engineer with a strong software engineering background and hands-on experience in agentic AI, LLM applications, and agent harness development.
The role focuses on designing and implementing AI-driven solutions, building reliable environments for coding agents, and integrating AI capabilities into software development workflows. You will work with modern AI frameworks, orchestration tools, and enterprise systems while contributing to architecture decisions, engineering best practices, and technical solution design.
We are seeking a proactive engineer who can independently solve technical challenges, evaluate emerging AI technologies, and collaborate effectively with engineering teams and stakeholders.


Requirements

• Hands-on experience developing LLM and RAG solutions, implementing agent workflows with orchestration and function calling, and configuring and using MCP integrations
• Hands-on experience in harness and agentic engineering: building and configuring agent harnesses, orchestrating sub-agents, managing agent state and tools, and implementing failure recovery
• Proficiency with Codex, Claude Code, Cursor, or similar coding agents in real development tasks, including configuring engineering environments, instructions, tools, and quality controls
• Strong Python or TypeScript/JavaScript skills, with experience developing and maintaining software solutions and working with Git and CI/CD
• A solid understanding of the modern software stack: backend, frontend, APIs, databases, and interactions between system components
• Knowledge of software architecture and system design, including decomposition, data flows, integration patterns, reliability, scalability, and security
• Hands-on experience with at least one cloud platform: Azure, AWS, or GCP; an understanding of application deployment, containerization, access management, and monitoring
• Experience integrating applications with external APIs and enterprise systems, including authorization, error handling, and data management
• Practical experience with automated testing, including unit, integration, and end-to-end tests
• Experience building feedback loops using automated checks, evals, tracing, and user feedback to iteratively improve the quality, reliability, and efficiency of agentic solutions
• Hands-on experience in agent governance: defining agent permissions, managing access to data and tools, setting limits on autonomy, implementing human-in-the-loop controls, and auditing agent actions
• Ability to work through technical tasks independently, justify architectural decisions, and collaborate with engineering teams and business stakeholders
• Advanced English for technical discussions, documentation, and client communication

Nice to have

• Experience in technical presales, discovery sessions, and shaping client AI initiatives
• Experience preparing technical proposals, effort estimates, and client presentations
• Experience delivering technical seminars, workshops, and training programs for engineering teams

Responsibilities

• Analyze engineering and business problems, assess the suitability of AI, and define requirements and solution architectures
• Develop LLM applications, RAG systems, and agent workflows; connect tools through MCP and integrate with enterprise services
• Build and configure agent harnesses, orchestrate sub-agents, and integrate coding agents into development and testing processes
• Research and evaluate models, frameworks, and AI tools against quality, security, performance, and cost criteria
• Introduce AI best practices and agent governance within project teams; build feedback loops based on testing, evals, and analysis of agent execution
• Analyze client requests from a technical perspective and prepare solution concepts, demonstrations, and presales materials
• Create architecture and technical documentation, contribute to the internal knowledge base, and share expertise with engineering teams

Availability expectations

• The role involves regular collaboration with US-based stakeholders, with availability until 10 PM Pacific Time