AI creates specialised engineering roles, not generalists: Andela study, TechGig

An analysis of 47,000 recent engineering job postings from Fortune 500 companies by Andela, an AI-native talent and services platform, indicates that artificial intelligence (AI) is leading to the emergence of specialised engineering roles. The research, released on Thursday, identified over 2,000 skills that converge into 23 new job titles, highlighting a shift away from generalist expectations in the AI era.

According to Cory Hymel, head of research at Andela, companies are increasingly reworking job titles and descriptions to align with the demands of safely and efficiently deploying AI through the software delivery lifecycle. This contrasts with the notion that AI would push engineers towards more generalist roles.

Emerging Engineering Roles

The study identified several key emerging engineering roles:

• MLOps pipeline engineer: Builds and manages automated infrastructure for deploying, versioning, and monitoring machine learning models in production, combining skills from ML, DevOps, and data engineering.
• LLM application engineer: Focuses on building and evaluating foundational models via large language model (LLM) application and conversation systems, blending AI engineer and ML engineer expertise.
• FinOps reliability engineer: Runs cloud infrastructure for both reliability and cost, requiring skills in DevOps, site reliability engineering (SRE), and cloud engineering.
• Docs-as-Code engineer: Applies program management and DevOps engineering skills to technical writing, enabling specification-driven development.
• Product frontend engineer: Integrates traditional frontend engineering with product management, UX research, and product design skills, owning the full user-facing feature lifecycle.

Hymel emphasised that while AI can automate some cross-role habitable skills, the skills directly focused on an engineer’s core role become more critical than ever. Additionally, soft business skills are gaining importance.

Rethinking Job Descriptions and Upskilling

The research suggests that generic job titles are detrimental in the current market, often leading to poor hiring outcomes. Companies that have not revised their job postings to be more specific and outcome-focused are at a disadvantage, potentially hiring the wrong person due to vague descriptions. Hymel advises organisations to focus on desired outcomes, prioritising soft skills and collaboration over rigid technical scores, as the “cost of code is going nearer to zero.”

Engineers should evaluate their current skill sets against these emerging roles and identify areas for upskilling. For instance, technical writers can pivot to Docs-as-Code engineer roles by acquiring program management and DevOps engineering skills. Similarly, back-end engineers can expand their focus to deployments, scalability, and infrastructure reliability, giving rise to roles like the polyglot back-end integration engineer. Product managers can also start contributing code, evolving into ‘product experience designers’. Engineering managers and HR professionals are advised to revise job descriptions to attract relevant AI-focused talent and consider how AI can augment human teams.