AI Summary
Lead technical direction and delivery of a team of engineers and QAs, shaping ML and AI pipelines, and setting architectural standards. Manage a team of 5-7 engineers and QAs, growing them technically and professionally. Strong technical depth and judgment required.
Key Highlights
Lead technical direction and delivery of a team
Manage a team of engineers and QAs
Shape ML and AI pipelines and set architectural standards
Key Responsibilities
Own the architecture, technical roadmap, and delivery of the team's backend and AI/ML systems
Lead the productionization of ML and AI work
Manage, mentor, and grow a team of engineers and QAs
Technical Skills Required
Benefits & Perks
Competitive, tax-free salary
Discretionary performance bonus
Comprehensive medical insurance
Nice to Have
Experience productionizing LLM-based / GenAI pipelines
Familiarity with recommendation systems, ranking, or personalization
Familiarity with observability stacks
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
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- 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
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- 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
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- 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
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