Machine Learning Engineer
Confirmed on the employer's own hiring board on Sep 25, 2026. First seen by Alion on Sep 11, 2026.
High Technical Impact: A unique professional challenge to build and operate production ML systems and agentic workflows for a stable, market-leading global enterprise.
Competitive Compensation: Competitive base salary and annual target bonus.
Benefits Package: Cafeteria allowance and private health insurance package.
Flexibility: Hybrid working model requiring 3 days of office presence (Budapest) and offering 2 days of Home Office per week.
Responsibilities
ML Pipeline Engineering: Build and maintain production ML pipelines - feature engineering, model training, evaluation, deployment, and monitoring on AWS and Databricks.
Agentic 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.
Feature Engineering Pipelines: Develop and maintain feature engineering pipelines, ensuring data quality, freshness, and consistency across batch and real-time serving paths.
MLOps Infrastructure: Operate and extend MLOps infrastructure: model CI/CD, experiment tracking, automated retraining, model versioning, and performance monitoring using MLflow and Databricks tooling.
LLM Applications: Build and maintain RAG pipelines, vector database integrations, prompt management systems, and tool-use components for LLM-powered applications.
Monitoring & Alerting: Implement model monitoring dashboards and alerting: tracking drift, latency, error rates, and cost, escalating anomalies with context.
Code 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.
Cross-Team Collaboration: Collaborate with data engineers on upstream pipeline dependencies and with data scientists on translating research outputs into deployable services.
Requirements
Required Experience
Industry Experience: 3+ years in machine learning engineering, applied ML, or software engineering with a meaningful ML focus and recent production delivery.
Production Deployment: Production experience deploying and maintaining ML models in a serving environment handling real inference traffic beyond the notebook stage.
Databricks & 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).
LLMs & 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.
Software 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.
Deployment & Infrastructure: Familiarity with containerized deployment (Docker, ECS, or Kubernetes) and infrastructure-as-code concepts.
Preferred Qualifications
Experience with real-time inference serving and performance tuning: latency profiling, model optimization, caching, and scaling.
Hands-on experience with evaluation frameworks for agentic systems: task success measurement, hallucination detection, and cost tracking.
Familiarity with streaming data systems (Kafka, Kinesis, Spark Structured Streaming) as they relate to real-time feature computation and online inference.
Experience with fine-tuning LLMs or training custom models on domain-specific data.
Exposure to data governance practices: lineage tracking, access controls, and model documentation within Unity Catalog or equivalent.
BS or MS in Computer Science, Machine Learning, or a quantitative field (practical production experience weighted equally).