Manager, Software Engineering (Data & AI/ML)

TalentOne; Dubai; Hybrid; AED 480,000 - 720,000; Full time

Benefits offered by this job

Medical insurance

Annual flight allowance

Visa sponsorship

Hybrid working

Learning budget

Job summary

TalentOne is seeking a hybrid leadership role blending technical leadership and people management. You’ll own the architecture and delivery of backend and AI/ML systems, design data and ML pipelines, and guide the team through productionization and scale.

Expect to mentor engineers while staying hands-on with critical services and prototypes. The role emphasizes deep Python expertise, FastAPI experience, and strong CI/CD discipline, with a focus on MLOps and AI pipelines involving LLMs.

Qualifications

• 6+ years of professional software engineering experience with backend and AI/ML systems exposure.
• Strong command of Python and asynchronous programming.
• Strong relational DB skills—PostgreSQL and MySQL.
• Production experience with Databricks/Spark.
• Experience building and orchestrating pipelines with Airflow; production experience with Kafka or similar.
• Experience with Python web frameworks, particularly FastAPI.
• Solid grounding in containerization and orchestration—Docker and Kubernetes.
• Ownership of deployment and delivery practices—CI/CD and release management.
• Working knowledge of data pipelines, AI/ML pipelines, and the MLOps lifecycle.
• Demonstrated technical leadership—driving architecture, mentoring engineers, and setting standards.

Responsibilities

• Own the architecture, technical roadmap, and delivery of the team’s backend and AI/ML systems, from design through production operation.
• Lead the productionization of ML and AI work—turning data scientists’ experimentation into reliable, maintainable services, standalone or integrated with dependent systems.
• Design and review data and ML pipelines: deterministic/classical ML pipelines as the core, plus complex data pipelines incorporating LLM calls.
• Set and uphold engineering standards — code quality, testing, observability, deployment practices, and MLOps lifecycle discipline.
• Own delivery for the team — planning, prioritization, and dependable execution against commitments.
• Stay hands-on: contribute directly to critical services, prototypes, and the hardest problems.
• Manage, mentor, and grow a team of ~5–7 engineers and QAs — career development, performance, hiring, and day-to-day delivery.
• Partner with product, business, other backend teams, and DevOps to align on priorities, interfaces, SLAs, and integration.
• Drive decisions on infrastructure, deployment (CI/CD), monitoring, and reliability across the team’s systems.

Skills

Python

Async programming

PostgreSQL

MySQL

Databricks

Spark

Airflow

Kafka

FastAPI

Redis

Docker

Kubernetes

CI/CD

MLOps

Technical leadership

Team mentoring

Tools

Databricks

PostgreSQL

MySQL

Docker

Kubernetes

FastAPI

Redis

Airflow

CI/CD

Spark

Job description

This is a hybrid leadership role — part tech lead and architect, part people manager. You’ll own the technical direction and delivery of the team: shaping how we productionize deterministic and classical ML pipelines (and increasingly, AI pipelines built around LLM calls), setting architectural standards, and making the calls on how our services scale and evolve.

You’ll manage a team of roughly 5–7 engineers and QAs, growing them technically and professionally, while staying hands‑on enough to lead by example in the codebase.

Expect roughly 70% technical leadership and architecture, 30% people management — with hands‑on engineering woven throughout. We’re looking primarily for technical depth and judgment. Formal people‑management experience is valuable but not the deciding factor — we’ll coach the right technically astute leader into the management side.

Responsibilities:

• Own the architecture, technical roadmap, and delivery of the team’s backend and AI/ML systems, from design through production operation.
• Lead the productionization of ML and AI work — turning data scientists’ experimentation into reliable, maintainable services, standalone or integrated with dependent systems.
• Design and review data and ML pipelines: deterministic/classical ML pipelines as the core, plus complex data pipelines incorporating LLM calls.
• Set and uphold engineering standards — code quality, testing, observability, deployment practices, and MLOps lifecycle discipline.
• Own delivery for the team — planning, prioritization, and dependable execution against commitments.
• Stay hands‑on: contribute directly to critical services, prototypes, and the hardest problems.
• Manage, mentor, and grow a team of ~5–7 engineers and QAs — career development, performance, hiring, and day-to-day delivery.
• Partner with product, business, other backend teams, and DevOps to align on priorities, interfaces, SLAs, and integration.
• Drive decisions on infrastructure, deployment (CI/CD), monitoring, and reliability across the team’s systems

Requirements

• 6+ years of professional software engineering experience, with strong backend and AI/ML systems exposure
• Deep technical command of Python and asynchronous programming
• Strong relational database skills — schema design, query optimization, and indexing (PostgreSQL and MySQL)
• Production experience with data processing at scale — Databricks/Spark
• Experience building and orchestrating pipelines with Airflow Production experience with stream processing — Kafka or equivalent
• Experience with Python web frameworks, particularly FastAPI Experience with in‑memory data stores such as Redis Solid grounding in containerization and orchestration — Docker and Kubernetes
• Ownership of deployment and delivery practices — CI/CD and release management
• Working knowledge of data pipelines, AI/ML pipelines, and the MLOps lifecycle
• Demonstrated technical leadership — driving architecture, mentoring engineers, and setting standards, whether as a lead, staff engineer, or manager
• Strong analytical thinking, problem-solving, and communication skills

Nice to have:

• Prior formal people‑management experience (though we’ll coach the right technical leader into this)
• Experience productionizing LLM-based / GenAI pipelines Familiarity with recommendation systems, ranking, or personalization
• Familiarity with observability stacks (Prometheus/Grafana or similar)
• Exposure to columnar data processing libraries (Polars, pandas) Gaming or SaaS domain experience
• Able to start in the role within 3 weeks

Benefits

• Competitive, tax‑free salary in line with the market
• Discretionary performance bonus, based on individual, team, and company performance
• Comprehensive medical insurance
• Annual flight allowance
• Visa sponsorship
• Annual leave Learning and conference budget
• Hybrid working