{"id":1308620,"url":"https://alion.io/job/tango-senior-ml-ai-engineer","title":"Senior ML / AI Engineer","company":{"id":2189487,"name":"Tango","domain":"tango.me","url":"https://alion.io/company/tango-me","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Comeet","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Limassol, Cyprus"],"countries":["CY"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":57000,"max_usd":143000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1507},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Context Engineering","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Hallucination","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LoRA","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Model Distillation","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"SFT","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"Aerospike","optional":true},{"name":"Apache Kafka","optional":true},{"name":"BigQuery","optional":true},{"name":"Docker","optional":true},{"name":"GCP","optional":true},{"name":"GitLab","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Hugging Face","optional":true},{"name":"Java","optional":true},{"name":"Jetpack Compose","optional":true},{"name":"Kotlin","optional":true},{"name":"Kubeflow","optional":true},{"name":"Kubernetes","optional":true},{"name":"Multimodal AI","optional":true},{"name":"MySQL","optional":true},{"name":"PEFT","optional":true},{"name":"Ray","optional":true},{"name":"Redis","optional":true},{"name":"Spring Boot","optional":true},{"name":"Swift","optional":true},{"name":"TensorRT","optional":true},{"name":"Triton","optional":true},{"name":"TypeScript","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-09-18T12:28:04Z","employer_posted_date":"2026-09-18","last_verified_at":"2026-10-04T23:23:37Z","board_verified":true,"closed_at":null,"days_open":16,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":16},"description":"Description\nTango is a market-leading live-streaming platform with 450+ million registered users, in an industry projected to reach $240 billion in the next couple of years. Founded in 2018, powered by 500+ people globally, built on best-in-class video technology that lets talented people everywhere create live content, engage their fans and monetize their talent. We push the limits to move the app from \"one of the top\" to \"the leader\" - and the biggest lever we have right now is how well we can put models into the product.\nAI is the product surface here, not a research sandbox.\nWe are forming a new team around a single premise: one engineer who is fluent with LLMs and able to train a purpose built model of their own can take an AI feature from \"this might work\" to production traffic - at the scale we run, where parts of the platform peak at 50k RPS and 450M+ registered users see the result.\nWe are not looking for a prompt specialist, and we are not looking for someone who only trains models and hands over a checkpoint. We want engineers who move freely between both: reach for a frontier LLM when it is the right answer, distil or train a small specialised model when latency, cost or accuracy say otherwise, and own the thing in production either way.\nWhy this role is different\nBoth halves of the job, in one person. LLM systems and your own trained models are not separate teams here. The interesting decisions live exactly on the line between them, and you get to make them.\nReal problems with real signal. Live video and audio, content moderation and trust & safety, recommendation and ranking, abuse and fraud, creator monetization. Large volumes of production data and immediate feedback on whether your model helped.\nA real toolbox. Frontier model APIs and open-weight models, GPU budget for training and serving, plus internal agents, MCP servers and evaluation tooling built in-house against our own data. Time to evaluate new tools - we adopt fast and drop fast.\nYou set the practice. This is a new role at Tango. How we evaluate models, gate releases, version prompts and datasets, and decide build-vs-buy is not written yet. You write it, and you share it with the wider engineering org. No ceremony. A feature starts as a goal, not a ticket. You decide with the product manager what is worth building, and then you build it.\nResponsibilities\nOwn the problem, not just the model. With the product manager, turn a goal into a solution: question the proposed approach, offer alternatives, and say what to leave out.\nBuild with LLMs. Context engineering, retrieval, tool use and agentic flows, structured outputs, guardrails - plus fine tuning and distillation when a general model is too slow, too expensive, or not good enough.\nTrain your own models. When a small, narrow, purpose-built model wins - moderation and safety classifiers, ranking and recommendation, audio and vision, abuse and anomaly detection - you own it from data collection and labelling strategy through training and evaluation.\nDecide build vs. buy, with numbers. Frontier API, open-weight model, or trained in-house: justified on quality, latency, unit cost and data-privacy constraints, not on preference.\nShip it to production. Serving under real load, latency and cost budgets, batching and quantization, safe rollout, and integration with the backend services and clients the feature touches.\nProve it works. Offline evaluation sets you build and defend, then online A/B. Monitoring for drift, regressions and feedback loops once it is live, and the honesty to roll something back.\nYou will not do this alone, and you are not expected to be an expert in all of it. Domain experts in backend, platform, infrastructure, data, web, Android and iOS are there to be asked, and those teams maintain the tooling, conventions and test platform for each surface.\nRequirements\nWhat we're looking for\nLLM ENGINEERING\nHands-on production experience with LLMs - not demos: retrieval, tool use and agents, structured output, prompt and context engineering under latency and cost constraints \nPractical adaptation of models: SFT / LoRA / preference tuning, and distilling a large model into a small one you can actually afford to serve \nYou build evaluations before you build confidence: task-specific datasets, sensible use of LLM-as-judge and awareness of where it lies, offline-to-online correlation \nClear-eyed about failure modes - hallucination, prompt injection, silent quality drift, benchmark overfitting - and you verify rather than trust \nTRAINING YOUR OWN MODELS\nYou have trained and shipped models on real, messy production data - classification, ranking or recommendation, CV, speech/audio, or abuse and anomaly detection \nFull ownership of the data path: collection, labelling strategy and label quality, splits, leakage, class imbalance, and the cost of getting these wrong \nSolid ML fundamentals and the ability to debug a model rather than retrain it and hope \nFluent in Python and PyTorch, and efficient with GPUs - you know what your training run costs and why \nPRODUCTION ENGINEERING\n5+ years of professional engineering overall, with genuine software engineering skill - this role does not end in a notebook \nServing models under real load: throughput, tail latency, batching, caching, quantization, and the trade-offs between them \nReproducible pipelines and orchestration; versioning of data, models and prompts; the ability to explain what exactly was deployed last Tuesday \nProduction monitoring for ML: drift, regression detection, feedback loops, and safe rollback \nComfortable working with the services and clients around your model - backend, web, Android and iOS - with AI tooling and expert support \nEnglish proficiency at Intermediate level or higher\nHOW YOU WORK\nDaily, hands-on use of AI coding agents in real production work, and the ability to scope tasks well for them Comfortable owning a feature end to end: design, implementation, evaluation, rollout, and the result You have lived with what you shipped rather than handing it over at the end \nDirect, easy collaboration with product managers, designers and data analysts \nWould be great to have\nReal-time or streaming inference on live video and audio; on-device or edge models \nContent moderation, trust & safety, or anti-fraud at consumer scale \nRecommendation and ranking systems for consumer feeds, with online experimentation \nMultimodal work: video, audio, speech, or text-and-image together \nServing and training infrastructure: vLLM, Triton, TensorRT, Ray, Kubeflow or similar \nBuilding or improving agentic development workflows - custom agents, MCP servers, evals - and sharing what works with other engineers \nEnough Java 17+ / Spring Boot to integrate cleanly with our backend, or readiness to ramp up quickly Live-streaming, video, high-scale consumer products, fintech or communication platforms \nDocker and Kubernetes, GCP and BigQuery, Aerospike \nTechnical stack\nML: Python, PyTorch, Hugging Face, GPU training and inference on GCP, experiment tracking and model registry, BigQuery for data.\nPlatform: Java 17-21 and Spring Boot, GCP, Apache Kafka, MySQL, Redis, Aerospike, Docker, Kubernetes, GitLab. Clients: React and TypeScript on web, Kotlin and Jetpack Compose on Android, Swift on iOS.\nHow we hire\nAn intro call. \nA 90-minute working session on a small, real problem over live-streaming data, which we hand you at the start. You work on your own machine, with your own AI tooling, sharing your screen. We are interested in how you frame the problem, what baseline you choose, what you ask us, and how you satisfy yourself that what you built actually works. Fluency with our particular stack is not scored, and there is nothing to prepare. \nA final conversation with the team, including a deep dive into a model or AI feature you took to production. \nWhat we offer\nStock options - every employee is a partner in the company \nCompetitive salary and performance review bonus \nHybrid working model: one day per week remotely \nOfficial employment in Cyprus: work permit visa for employees and residence permit for spouses Medical insurance (100% for employees, 75% for family members) \nComfortable office in Limassol centre with lunch, dinner and snacks \nOpportunities for professional growth, Greek lessons, sport compensation \nFamily corporate events - we believe our spouses are part of our professional success \nTango is Title Sponsor of Aris Limassol F.C.: free attendance at matches, VIP and Sky Box access, events and merchandise \nGifts for employees \nWhen you apply\nTell us about one model or LLM feature you took all the way to production - and about one time your evaluation said it worked and production said otherwise.","description_format":"text","description_chars":8731,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"English","level":"Advanced (C1)","optional":false}]},"benefits":["Health insurance","Hybrid work","Stock options"],"hiring_locations":[{"name":"Cyprus","iso":"CY","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Streaming & OTT Platforms"],"lifecycle":[{"event":"open","at":"2026-09-26T15:13:26Z"}],"visa":[],"liveness":{"score":89,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.886,"p_room":1,"age_days":15,"expected_fill_days":46,"reasons":["conf:0","velocity","win:early"],"computed_at":"2026-10-04T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/tango-senior-ml-ai-engineer","json_url":"https://alion.io/job/tango-senior-ml-ai-engineer.json","meta":{"generated_at":"2026-10-05T00:10:54Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":90,"day_limit":5000,"remaining_today":4910,"minute_limit":60,"resets_at":"2026-10-06T00:00:00Z"}}}