AI Software Engineer

• Bachelor’s degree or equivalent experience (post-graduate study a plus); 3+ years in software engineering, machine learning, or similar.
• Strong Python, with real experience building backend services and RESTful APIs (FastAPI, Flask, or similar) - other stacks (TypeScript, Node, Go) a plus.
• A track record of shipping backend systems that stayed up, not just got built - you’ve owned services in production.
• Experience integrating AI/ML services (internal LLMs/SLMs, STT, TTS, NMT, embeddings) and third-party APIs into real systems.
• Hands-on with containerisation and orchestration (Docker, Kubernetes) and cloud-native design.
• Cloud experience, ideally Google Cloud Platform (GKE, Cloud Run, Pub/Sub, BigQuery, Vertex AI, or similar).
• Comfortable across database types (relational, NoSQL, and/or vector) and designing efficient data access patterns.
• Familiar with event-driven and microservices architectures, and both sync and async processing.
• You set up CI/CD without being asked, instrument what you ship, and think about security by default.
• A clear, precise, structured communication style, and the instinct to share what you know.
• English C1 (verbal and written); additional languages a plus.

You’re probably already a strong engineer. You don’t need this job to prove you can build. What you want is a problem big enough to be worth your craft - AI in production, at scale, done properly. That’s what this role offers. Start something brilliant with us.

Please include a full CV in English with your application.

• Design and build production-grade backend services and APIs that expose AI/ML capabilities: LLMs, SLMs, STT, TTS, NMT, embeddings, classification - to our products and external systems.
• Take research prototypes from the AI/ML team and turn them into scalable, secure, reliable software that survives real traffic.
• Integrate AI services with robust error handling, monitoring, graceful degradation, and clear fallback paths for when a model is slow, down, or wrong.
• Design and optimise data and model-serving pipelines for performance, reliability, and cost.
• Own deployment: CI/CD, environment management, and the automation.
• Make AI systems observable - logging, tracing, metrics, dashboards, and alerts on latency, reliability, cost, and quality.
• Treat evaluation as first-class: build the harnesses, quality gates, and regression tests that catch hallucinations, latency regressions, and quality drift before customers do.
• Keep it safe and compliant: secure handling of PII, access control, encryption, GDPR-aligned practice.
• Make the team compound - code reviews, technical talks, and mentoring that turn individual learning into team leverage.

Whats in it for you!

• The chance to define how AI actually reaches production here - not maintain a pipeline someone else drew, but set the patterns the whole AI platform is built on.
• Work on systems where your decisions are visible: the difference between an AI feature customers rely on and one they learn to distrust runs through your code.
• Real proximity to the model work - close enough to the AI/ML team to learn the frontier, owning the engineering that makes it usable.
• A platform problem worth solving, at the scale of millions of customer conversations.
• International, inclusive environment with continuous development through learning platforms and external training.
• Hybrid working model, competitive salary, flexible schedule, and a modern office with space to take a break.
• Corporate benefits and discounts.

Everyone’s racing to build with AI. Far fewer people can make it hold up in production: handle real traffic, fail gracefully, stay observable at 12:34am, and cost what it’s supposed to cost. That gap, between a model that demos well and a service customers can actually depend on, is where this role lives.

At Transcom, our AI/ML Engineers and Researchers push the models forward. You build everything around them that turns a clever prototype into software people trust: the APIs, the pipelines, the fallbacks, the telemetry. The craft that decides whether an AI feature ships or stalls. If you’re a strong backend engineer who’s genuinely curious about AI but would rather own the system than tune the model, this is the seat for you.

You’ll own the engineering: how AI capabilities get exposed, integrated, deployed, and kept alive, on an event-driven platform that processes customer interactions at scale. You’ll work closely with the AI/ML team, but the software craft is yours., At Transcom, we’re relentlessly committed - to our clients and each other. Every day, someone starts their journey here, turning today’s potential into tomorrow’s skills, getting recognised for working hard, being a team player, and championing positive change in their teams and communities. You’re included, just as you are, from day one. With the right mindset, there’s no end to how far we can go together.