{"id":3407824,"url":"https://alion.io/job/faktion-junior-ai-engineer","title":"Junior AI Engineer","company":{"id":54451,"name":"Faktion","domain":"faktion.com","url":"https://alion.io/company/faktion","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Recruitee","truth_index":{"grade":"B","score":79,"open_postings":11,"ghost_share":0.273,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":363,"computed_at":"2026-11-01T06:10:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Antwerp, Belgium"],"countries":["BE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Computer Vision","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Azure","optional":true},{"name":"Azure DevOps","optional":true},{"name":"CI/CD","optional":true},{"name":"GitHub","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Image Segmentation","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"OpenCV","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"PyTorch","optional":true},{"name":"RAG","optional":true},{"name":"Synthetic Data","optional":true},{"name":"TensorFlow","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-03-26T10:18:15Z","employer_posted_date":"2026-03-26","last_verified_at":"2026-11-01T03:53:04Z","board_verified":true,"closed_at":null,"days_open":219,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":219},"description":"As a Junior AI Engineer at Faktion, you'll jump-start your career working on a wide variety of hands-on projects across industries like manufacturing, finance, and operations. You won't just build prototypes-you will do real engineering, ship models to production, and build systems that deliver real-world impact. As a junior member of our team, you will be paired with experienced senior engineers and technical leads who will mentor, guide, and support your professional growth as you scale up your technical skill set.\nKey Responsibilities\nEnd-to-End AI Engineering: Design and implement end-to-end AI solutions spanning GenAI, classical ML, computer vision, and agentic systems tailored to enterprise customers, working alongside senior architects.\n\nArchitecture & Scoping: Work with senior engineers and product managers to translate client problems into concrete technical architectures and clear technical requirements.\n\nProduction Backends: Develop and deploy production-ready AI backends using Python, FastAPI, and Docker, focusing on scalability, code quality, and system reliability.\n\nEvaluation Pipelines: Build and maintain continuous evaluation frameworks (including LLM-as-a-Judge agents) to track model quality from development to production.\n\nMLOps & Pipeline Maintenance: Build and maintain reliable MLOps workflows covering data preprocessing, feature extraction, model training, and performance monitoring.\n\nContinuous Learning & Research: Research emerging advancements in Generative AI, machine learning, computer vision, and deep learning, identifying practical applications for client projects.\n\nCross-Functional Collaboration: Collaborate closely with software engineers, designers, and business stakeholders to ensure technical implementations match customer goals.\n\nTechnical Documentation: Author clear technical documentation, architectural notes, and presentations to explain complex AI concepts to both technical and non-technical stakeholders.\n\nMentorship & Professional Growth: Actively participate in structured 1-on-1 mentorship, code reviews, and pair programming sessions with senior team members to continually elevate your engineering practices.\n\nRequirements\nYou have a degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. A Master degree is a plus.\n\nYou have a strong curiosity about new technologies and you are a fast learner.\n\nYou are proficient in Python and have experience with relevant frameworks such as PyTorch, TensorFlow, OpenCV, LangChain, or LangGraph.\n\nYou are comfortable working across the AI stack, from prompt engineering and RAG architecture to model training, evaluation pipelines, and cloud deployment.\n\nYou are familiar with cloud-based platforms and services, such as Azure.\n\nYou have experience with containerisation tools such as Docker and API development frameworks such as FastAPI.\n\nYou possess strong problem-solving skills and can think critically and creatively to develop innovative AI solutions.\n\nYou have strong communication skills and can effectively convey complex technical concepts to a diverse audience.\n\nYou possess a proactive mindset and can work independently and collaboratively in a fast-paced, dynamic environment.\n\nExcellent communication skills in English (Dutch is a strong plus).\n\nFollowing experiences are a plus:\nYou have solid hands-on experience building and deploying AI systems, whether GenAI, classical ML, computer vision, or a combination.\n\nShowing your passion through GitHub repos, open-source contributions, or prior engagements in Kaggle competitions.\n\nExperience with computer vision techniques such as synthetic data generation, object detection, or image segmentation.\n\nExperience with MLOps tooling, including CI/CD pipelines, Azure DevOps, GitHub Actions, or infrastructure-as-code tools like Terraform.\n\nExposure to agentic frameworks and multi-agent system design.\n\nPublications in relevant AI/ML conferences and journals.\n\nExperience in one of our focus domains: Data Quality, Retail, Manufacturing, Finance.\n\nWe offer:\nA rewarding salary package that includes additional perks like a company car and fuel card or a mobility budget, comprehensive hospitalisation, group insurance, and a top-tier laptop and smartphone.\n\nBenefit from a company culture that stimulates both individual and team development, fostering professional growth.\n\nUtilise your innovation budget for engaging in exciting, educational, and challenging open-source projects.\n\nParticipate in team-building activities and gatherings, a great opportunity to unwind and engage with our vibrant team initiatives.\n\nA hybrid working policy where only 2 days a week are mandatory office days (Tuesdays and Thursdays).\n\nAt Faktion we build AI, and we use it thoughtfully in our own processes too. During your application, AI tools may help us with practical steps, like checking that your documents came through complete. They never decide who moves forward: every application is read by a real person on our team, and every decision is made by a human. Curious how this works, or how your data is handled? 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