- Translate ambiguous business problems into well-defined ML/DS problems. Investigate the real issue before proposing a solution, scope the right approach, and know when ML is the wrong answer.
- Set technical direction for ML initiatives in your problem area. Make build-vs-buy, ML-vs-heuristic, and model-architecture decisions based on cost, latency, complexity, and business impact — not novelty.
- Own ML systems end-to-end in production: data ingestion, training, serving, monitoring, retraining, and rollback. Anticipate and design around failure modes — data drift, train/serve skew, feedback loops, cold start, and label leakage — before they hit users.
- Build and ship the services, APIs, and integrations around your models, working across the stack to deliver ML-powered features users actually touch.
- Define the metrics that matter — offline evaluation, online A/B tests, and long-term business KPIs — and defend them. Question metrics you're handed when they don't reflect real user or business value.
- Partner with product, design, and business stakeholders to shape the roadmap, not just execute it. Translate model behavior and data insights into narratives that drive decisions at both tactical and executive levels.
- Raise the technical bar of the team through design reviews, code reviews, and mentorship. Your output is measured by what you ship and by what you unblock and elevate in others.
- At least 5+ YOE working as ML/AI engineer role
- Fluent use of AI coding assistants and LLM-based tools in your daily workflow (Claude Code, Copilot, or equivalent). At Chotot, AI is a compulsory tool in product development.
- Hands-on experience owning ML systems end-to-end in production environments
- Excellent English communication — you will work directly with cross-country teams
- Experience building and scaling ML-powered products from early-stage development to large-scale production usage, with strong ownership across product iteration, system reliability, and business impact.
- Track record of ML systems you personally took from problem framing to production and measurable business impact — not just trained models, but shipped features users interacted with.
- Experience Python and SQL, with hands-on experience in a cloud data warehouse (BigQuery preferred) and a public cloud (GCP preferred).
- Solid grasp of both classical ML and deep learning. You pick the right tool for the constraint rather than defaulting to the latest paper.
Nice-to-have
- Experience with vector databases and embedding-based retrieval.
- Familiarity with event-driven and streaming data pipelines.
- Prior experience mentoring or setting technical direction.
Thank you for taking your time to read our job description and thank you in advance if you decide to apply for this position. Shortlisted candidates will be contacted within 2 weeks since application, otherwise we might meet when another chance arises.
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