{"id":1191732,"url":"https://alion.io/job/randstad-hungary-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":2215937,"name":"Randstad Hungary","domain":"randstad.hu","url":"https://alion.io/company/randstad-hu","size_band":"1001-5000","is_staffing_agency":false,"is_intermediary":false,"listed_via":null,"ats_vendor":"Traffit","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"Function Calling","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"Tool Use","optional":false},{"name":"XGBoost","optional":false},{"name":"Amazon Kinesis","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Hallucination","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-09-11T06:23:11Z","employer_posted_date":"2026-09-11","last_verified_at":"2026-09-25T00:55:17Z","board_verified":true,"closed_at":null,"days_open":13,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":13},"description":"High Technical Impact: A unique professional challenge to build and operate production ML systems and agentic workflows for a stable, market-leading global enterprise.\n\nCompetitive Compensation: Competitive base salary and annual target bonus.\n\nBenefits Package: Cafeteria allowance and private health insurance package.\n\nFlexibility: Hybrid working model requiring 3 days of office presence (Budapest) and offering 2 days of Home Office per week.\n\nResponsibilities\nML Pipeline Engineering: Build and maintain production ML pipelines - feature engineering, model training, evaluation, deployment, and monitoring on AWS and Databricks.\n\nAgentic AI Workflows: Implement and operate agentic AI workflows using frameworks such as LangChain, LangGraph, or equivalent, following architectural patterns established by senior engineers while contributing improvements based on production observations.\n\nFeature Engineering Pipelines: Develop and maintain feature engineering pipelines, ensuring data quality, freshness, and consistency across batch and real-time serving paths.\n\nMLOps Infrastructure: Operate and extend MLOps infrastructure: model CI/CD, experiment tracking, automated retraining, model versioning, and performance monitoring using MLflow and Databricks tooling.\n\nLLM Applications: Build and maintain RAG pipelines, vector database integrations, prompt management systems, and tool-use components for LLM-powered applications.\n\nMonitoring & Alerting: Implement model monitoring dashboards and alerting: tracking drift, latency, error rates, and cost, escalating anomalies with context.\n\nCode Quality & Production Operations: Write production-quality Python code with thorough testing, documentation, and adherence to team engineering standards. Participate in on-call rotations for ML systems, triaging production issues and implementing fixes with appropriate urgency.\n\nCross-Team Collaboration: Collaborate with data engineers on upstream pipeline dependencies and with data scientists on translating research outputs into deployable services.\n\nRequirements\nRequired Experience\nIndustry Experience: 3+ years in machine learning engineering, applied ML, or software engineering with a meaningful ML focus and recent production delivery.\n\nProduction Deployment: Production experience deploying and maintaining ML models in a serving environment handling real inference traffic beyond the notebook stage.\n\nDatabricks & AWS Ecosystem: Working proficiency with the Databricks ML ecosystem (MLflow, Model Serving, Feature Store) and familiarity with supporting AWS services (SageMaker, S3, Lambda, Step Functions, ECS/EKS).\n\nLLMs & Agentic AI: Hands-on experience building applications with LLMs: RAG implementations, vector databases, prompt engineering, and API-based model integration (OpenAI, Anthropic, Bedrock, or equivalent). Production exposure to agentic AI patterns: multi-step orchestration, tool use, or workflow automation using LangChain, LangGraph, or similar frameworks.\n\nSoftware Engineering & Core ML: Strong Python proficiency and solid software engineering fundamentals: version control, testing, CI/CD, code review, and debugging production systems. Experience with at least one core ML framework (PyTorch, TensorFlow, scikit-learn, XGBoost) applied to production prediction tasks.\n\nDeployment & Infrastructure: Familiarity with containerized deployment (Docker, ECS, or Kubernetes) and infrastructure-as-code concepts.\n\nPreferred Qualifications\nExperience with real-time inference serving and performance tuning: latency profiling, model optimization, caching, and scaling.\n\nHands-on experience with evaluation frameworks for agentic systems: task success measurement, hallucination detection, and cost tracking.\n\nFamiliarity with streaming data systems (Kafka, Kinesis, Spark Structured Streaming) as they relate to real-time feature computation and online inference.\n\nExperience with fine-tuning LLMs or training custom models on domain-specific data.\n\nExposure to data governance practices: lineage tracking, access controls, and model documentation within Unity Catalog or equivalent.\n\nBS or MS in Computer Science, Machine Learning, or a quantitative field (practical production experience weighted equally).","description_format":"text","description_chars":4240,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Cafeteria","Health insurance","Home office","Hybrid work"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-24T17:00:12Z"}],"liveness":{"score":70,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.774,"p_room":0.9,"age_days":13,"expected_fill_days":27,"reasons":["conf:2","win:mid"],"computed_at":"2026-09-25T03:17:02Z"},"pay":null,"html_url":"https://alion.io/job/randstad-hungary-machine-learning-engineer","json_url":"https://alion.io/job/randstad-hungary-machine-learning-engineer.json","meta":{"generated_at":"2026-09-25T03:17:02Z","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":3613,"day_limit":5000,"remaining_today":1387,"minute_limit":60,"resets_at":"2026-09-26T00:00:00Z"}}}