{"id":3867031,"url":"https://alion.io/job/control-risks-ai-engineer","title":"AI Engineer","company":{"id":6732,"name":"Control Risks","domain":"controlrisks.com","url":"https://alion.io/company/control-risks","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":82,"open_postings":14,"ghost_share":0,"stale_share":0.714,"repost_share":0,"time_to_fill_p50_days":35,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Berlin, Germany"],"countries":["DE"],"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":".NET","optional":false},{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Claude","optional":false},{"name":"Claude Code","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"GitHub","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"Hugging Face","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Neo4j","optional":false},{"name":"NER","optional":false},{"name":"Notion","optional":false},{"name":"OpenAI","optional":false},{"name":"Perplexity","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Sentence-Transformers","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tavily","optional":false},{"name":"Transformers","optional":false},{"name":"Vertex AI","optional":false},{"name":"C#","optional":true}],"status":"live","first_seen_at":"2026-11-06T12:39:38Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-06T19:56:37Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"As AI Engineer you will own the AI and data layer of the platform. You will design and implement the knowledge graph architecture, build agentic pipelines that enrich entity data through AI web research, and develop the graph interrogation layer that translates natural language compliance questions into structured graph queries.\nYou will work closely with the CTO and a small AI team including a data engineer and a junior AI engineer. You will have significant technical autonomy and direct influence over how the product evolves.\nPlease submit yoru CV in English.\nRequirements\nWhat You’ll Work On\nKnowledge graph architecture and implementation\nOwn and evolve the platform’s knowledge graph data model - entities (Company, Person, Community, Episodic), relationships, and temporal attributes\nImplement and extend custom entity and relationship extraction pipelines using LMs, with structured output validation and confidence scoring\nUse Graphiti (Zep AI’s temporal knowledge graph framework) as the memory layer, managing episode ingestion, entity resolution, and graph updates\nDesign and enforce graph schema standards, ensuring consistency across data sources and ingestion pipelines\nBuild evaluation frameworks to measure extraction quality, entity disambiguation accuracy, and graph coverage\nAgentic AI pipelines\nDesign and implement multi-step agentic workflows that orchestrate LLM calls, web search, database lookups, and graph writes\nIntegrate AI web search (Tavily, Perplexity, or similar) as a tool within agentic pipelines for real-time entity enrichment\nBuild retrieval-augmented generation (RAG) pipelines over the knowledge graph, translating compliance queries into Cypher and natural language answers\nImplement the graph interrogation layer - a conversational interface for compliance analysts to query the knowledge graph without writing Cypher\nManage LLM API integrations (Anthropic Claude, Gemini) including prompt engineering, structured outputs, and cost/latency optimisation\nData pipelines and integrations\nBuild and maintain Python-based data ingestion pipelines reading from Databricks, connecting to external APIs (sanctions lists, corporate registries, Polixis, Vantage), and writing to Neo4j\nImplement NER (named entity recognition) and entity disambiguation logic for extracting structured facts from unstructured compliance documents\nDevelop topic modelling pipelines to classify extracted facts as knowledge graph relationship attributes\nIntegrate with the .NET backend via well-defined HTTP contracts, ensuring the AI layer is independently deployable and testable\nResearch and technical leadership\nStay current with the fast-moving agentic AI and knowledge graph literature - evaluate new frameworks, models, and techniques for production applicability\nContribute to architectural decisions with evidence: benchmarks, prototypes, and documented trade-offs\nMaintain Claude Code context files (CLAUDE.md, SKILLS.md) for the AI codebase, enabling AI-augmented development across the team\nWrite clear technical documentation in Notion covering design decisions, data models, and operational runbooks.\nWhat We’re Looking For\nMust-have\n3+ years of professional experience in AI/ML engineering, with at least 1 year working on knowledge graphs or graph-based data systems in production\nHands-on experience with Neo4j - data modelling, Cypher query writing, schema design, and performance tuning\nDeep familiarity with LLM APIs (OpenAI, Anthropic, or Gemini) - prompt engineering, structured outputs, function/tool calling, and cost management\nUnderstanding the trade-offs between LLMs and smaller fine-tuned models (Hugging Face Transformers, sentence-transformers) - knowing when a purpose-built model outperforms a prompted general one on cost, latency, and accuracy\nExperience building agentic pipelines - multi-step LLM workflows with tool use, memory, and state management\nPython proficiency - you write clean, testable Python and understand async patterns for I/O-bound AI workloads\nExperience with RAG architectures - vector search, hybrid retrieval, and integrating retrieval into LLM-powered applications\nAbility to read and apply AI research papers - you can take a paper on narrative extraction or entity disambiguation and turn it into a working prototype\nStrong English communication skills - written and verbal\nStrong advantage\nDirect experience or knowledge with Graphiti (Zep AI) or other temporal knowledge graph frameworks\nFamiliarity with entity resolution and disambiguation techniques at scale\nExperience with NER pipelines, topic modelling, or information extraction from compliance or financial documents\nKnowledge of the third-party risk, sanctions screening, or KYC/AML domain\nExperience with Databricks, Delta Lake, or similar data lakehouse platforms\nExposure to GCP (Vertex AI, Cloud Run, Pub/Sub, Cloud Storage)\nExperience designing evaluation frameworks for LLM-generated structured outputs\nPrior work in a RegTech, FinTech, or compliance-adjacent environment\nWhat Good Looks Like in This Role\nWe are direct about what we expect. The person who thrives here will:\nTreat the knowledge graph data model as the product’s most important artefact - they think carefully about schema decisions and document them\nRead the literature. When evaluating a new approach to entity disambiguation or graph memory, they check what’s been published, not just what’s on GitHub\nWrite evaluation harnesses before scaling pipelines. They know that a pipeline that produces plausible-looking output is not the same as one that produces correct output\nCommunicate blockers and trade-offs early. Technical uncertainty is expected - silence is not\nBenefits\n\n Control Risks offers a competitively positioned compensation and benefits package that is transparent and summarised in the full job offer.\n We operate a discretionary global bonus scheme that incentivises, and rewards individuals based on company and individual performance.\n Control Risks supports hybrid working arrangements, wherever possible, that emphasise the value of in-person time together - in the office and with our clients - while continuing to support flexible and remote working.\n Working with our clients, from day one our people are given direct responsibility, career development and the opportunity to work collaboratively on fascinating projects in a rewarding and inclusive global environment.\n The Company is committed to further its diversity, inclusion and equity agenda and as an employee you have access to various business resource groups in the Company including the Company's LGBT+ network called MOSAIC, the Women's Network, and Synergy - a people network to celebrate and promote ethnic diversity at Control Risks\n Control Risks is committed to a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, colour, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age or veteran status.\nIf you require any reasonable adjustments to be made in order to participate fully in the interview process, please let us know and we will be happy to accommodate your needs.","description_format":"text","description_chars":7247,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"English","level":"Upper-Intermediate (B2)","optional":false}]},"benefits":["Equity","Hybrid work"],"hiring_locations":[{"name":"Germany","iso":"DE","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Security Services","Management Consulting"],"lifecycle":[{"event":"open","at":"2026-11-06T12:39:38Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":35,"reasons":["conf:1","win:early"],"computed_at":"2026-11-06T21:13:42Z"},"pay":null,"html_url":"https://alion.io/job/control-risks-ai-engineer","json_url":"https://alion.io/job/control-risks-ai-engineer.json","meta":{"generated_at":"2026-11-06T21:13:42Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":1323,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-07T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":6732},"rest":"https://alion.io/mcp/rest/get_company?id=6732"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fcontrol-risks-ai-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fcontrol-risks-ai-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fcontrol-risks-ai-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/control-risks-ai-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fcontrol-risks-ai-engineer"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3867031}},{"tool":"search_jobs","arguments":{"company":"controlrisks.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3867031},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}