Accountabilities

You will lead the technical design and delivery of chat-team projects while remaining closely involved in implementation. Your responsibilities will include:

Own the technical design, architecture, and delivery of chat-team projects.

Help define engineering priorities, standards, and day-to-day technical decisions.

Translate product requirements into simple, scalable, and maintainable system designs.

Write high-quality production Python code and develop clear abstractions without unnecessary complexity.

Review pull requests, assessing correctness, maintainability, test coverage, and overall design quality.

Mentor junior developers, providing practical guidance on programming fundamentals, system design, and code quality.

Diagnose production issues across application code, agent workflows, prompts, models, data, and infrastructure.

Improve system reliability, observability, latency, and operating costs.

Promote effective use of AI coding agents across exploration, planning, implementation, testing, debugging, and code review.

Identify opportunities to automate and improve engineering workflows while maintaining accountability for code quality.

Help the team adopt responsible, effective agentic coding practices and become more AI-native.

Requirements

The ideal candidate combines strong software engineering fundamentals with hands-on experience building LLM-powered applications and using AI coding agents. The following qualifications and skills are expected:

Approximately 4–5 years of professional software engineering experience.

Strong proficiency in Python, including idiomatic, typed, testable, and maintainable production code.

Solid programming fundamentals and consistently sound engineering judgment.

Experience designing, delivering, and operating production systems.

Strong understanding of API design, data modeling, concurrency, error handling, and observability.

Ability to create useful abstractions while keeping systems simple and understandable.

Experience writing automated tests using tools such as pytest.

Ability to conduct meaningful code reviews and clearly explain technical recommendations.

Experience mentoring junior developers and helping improve their engineering capabilities.

Strong ownership, initiative, problem-solving skills, and the ability to drive improvements across a team.

Clear written and verbal communication skills.

Agentic Systems Experience

Hands-on experience building LLM-powered or agentic applications is required. Relevant experience includes:

Building tool-calling or multi-step workflows using LangGraph or comparable agent frameworks.

Designing conversation state, context management, and execution flows.

Working with model APIs, structured outputs, retrieval, and grounding techniques.

Supporting streaming responses and asynchronous execution.

Evaluating prompts, models, and end-to-end agent behavior.

Instrumenting and debugging LLM applications with Langfuse, LangSmith, or similar observability platforms.

Managing the reliability, latency, and cost of production LLM systems.

Expertise in every named tool is not expected. A strong understanding of the underlying engineering principles and the ability to adapt as the ecosystem evolves are more important.

Agentic Coding Experience

The candidate should be highly proficient in agentic coding workflows and understand how to use coding agents as engineering tools rather than simply as code generators. This includes:

Using coding agents effectively for exploration, planning, implementation, testing, debugging, and code review.

Providing agents with appropriate context, constraints, and verification steps.

Critically evaluating generated code for correctness, security, maintainability, and unnecessary complexity.

Designing development workflows that ensure engineers remain accountable for the code produced.

Identifying repeatable team processes that can be improved through agents and automation.

Teaching junior developers to use coding agents effectively without compromising their engineering fundamentals.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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