{"id":3002403,"url":"https://alion.io/job/circle-k-senior-aiml-engineer","title":"Senior AI/ML Engineer","company":{"id":1785121,"name":"Circle K","domain":"circlek.com","url":"https://alion.io/company/circlek-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Warsaw, Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":55000,"max_usd":93000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":59},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Chroma","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Computer Vision","optional":false},{"name":"CrewAI","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI","optional":false},{"name":"Pinecone","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":"Structured Outputs","optional":false},{"name":"TensorFlow","optional":false},{"name":"Vertex AI","optional":false},{"name":"Weaviate","optional":false},{"name":"AutoGen","optional":true},{"name":"Docker","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"LoRA","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"PEFT","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"RLHF","optional":true},{"name":"Vertex AI Agent Builder","optional":true}],"status":"live","first_seen_at":"2026-10-15T00:00:00Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-23T10:26:54Z","board_verified":true,"closed_at":null,"days_open":8,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":8},"description":"Job Description\nWe're looking for an AI/ML Engineer to design, build, and deploy machine learning systems and intelligent agentic solutions that solve real business problems. This role is centered on agentic AI - building LLM-powered systems that can reason, plan, and autonomously take action across multiple AI platforms and ecosystems - combined with classic ML engineering across the full lifecycle, from data pipelines and model development to production deployment. You'll work closely with our AI Platform Architect to align implementation with technical strategy and architecture standards, and partner directly with business stakeholders to ensure the solutions you build translate into real operational impact.\nKey Responsibilities\nAgentic AI & LLM Systems (Core Focus)\nDesign and build agentic workflows that use LLMs to plan, reason, and execute multi-step tasks autonomously\nBuild and deploy agentic solutions across multiple AI platforms and ecosystems (e.g., Azure AI Foundry, Google Cloud Vertex AI / Gemini, Anthropic Claude), selecting the right platform and tools for the use case\nDevelop tool-use and function-calling integrations, connecting agents to APIs, databases, and internal systems\nImplement orchestration patterns (e.g., planner-executor, multi-agent collaboration, ReAct-style reasoning loops) for complex task automation\nBuild retrieval-augmented generation (RAG) pipelines to ground agent outputs in proprietary or real-time data\nEstablish guardrails, evaluation harnesses, and monitoring for agent reliability, safety, and task success rates\nPrototype and iterate quickly on agent behaviors using frameworks such as LangGraph, LlamaIndex, CrewAI, or custom orchestration layers\nDesign agentic workflows that consume structured outputs from perception/detection models (e.g., computer vision systems) to trigger downstream actions such as alerts, corrective tasks, or automated reports\nModel Development & MLOps\nDesign, train, fine-tune, and evaluate machine learning and deep learning models for production use cases, including computer vision / object detection models for image-based recognition and classification tasks\nBuild and maintain scalable data pipelines for training, validation, and inference\nImplement MLOps best practices: CI/CD for models, versioning, monitoring, and automated retraining\nOptimize model performance, latency, and cost across training and inference workloads, including for on-device or edge/mobile deployment scenarios\nDesign pipelines to compare model-detected states against a source-of-truth dataset and flag discrepancies, feeding results into downstream automation\nContinuously improve model accuracy using field/production feedback, labeled data pipelines, and active learning loops\nApplied Engineering\nCollaborate with product and design teams to translate business requirements into AI-powered features\nIntegrate ML models and agents into production applications via robust, well-tested APIs\nWrite clean, maintainable, well-documented code and contribute to internal tooling/libraries\nConduct A/B tests and offline/online evaluations to measure model and agent impact\nCollaboration & Communication\nWork closely with the AI Platform Architect to ensure solutions align with overall AI architecture, technical standards, and long-term platform strategy\nPartner directly with business stakeholders to understand operational needs, define success metrics, and ensure delivered solutions drive measurable business value\nPartner with data engineers, product managers, and domain experts to define success metrics\nCommunicate technical trade-offs and results clearly to both technical and non-technical stakeholders\nStay current with emerging research in ML, LLMs, and agentic architectures, and evaluate their applicability\nRequired Qualifications\nBachelor's or master's degree in computer science, Machine Learning, or related field (or equivalent experience)\n5+ years of experience building and deploying ML models in production\nStrong programming skills in Python; familiarity with ML frameworks (PyTorch, TensorFlow, scikit-learn)\nHands-on experience building and deploying agents on at least one major AI platform (e.g., Azure AI Foundry, Google Vertex AI / Gemini, Anthropic Claude), with strong interst in working across multiple\nExperience with LLM APIs and prompt engineering across providers (OpenAI, Anthropic, Google)\nFamiliarity with vector databases and RAG architectures (e.g., Pinecone, Weaviate, Chroma)\nExperience with computer vision or image recognition models in a production setting\nSolid understanding of software engineering fundamentals: version control, testing, API design\nExperience with cloud platforms (AWS, GCP, or Azure)\nPreferred Qualifications\nExperience building agents on Azure AI Foundry (Agent Service) and/or Google Vertex AI Agent Builder\nFamiliarity with cross-platform agent portability - adapting the same agent logic/tooling across different model providers and orchestration layers\nExperience with multi-agent orchestration frameworks (LangGraph, AutoGen, CrewAI)\nExperience designing agentic systems that combine perception (vision/detection) outputs with reasoning/action layers\nBackground in reinforcement learning or fine-tuning LLMs (LoRA, RLHF)\nExperience with containerization and orchestration (Docker, Kubernetes)\nContributions to open-source ML/AI projects\nFamiliarity with evaluation frameworks for LLM/agent outputs\nExperience optimizing models for on-device or edge deployment (e.g., mobile/tablet applications)\nWhat do we offer?\nContract of employment\nAnnual bonus \nPrivate medical care \nCafeteria Platform/Multisport\nEnglish lessons subsidized by the company \nGroup insurance \nTwo additional days off (Good Friday, Friday after Corpus Christi) - with the possibility of exchanging for other holidays\nEmployee Referral Bonus Program\nAttractive discounts for products and services at our stations \nEmployee stock purchase plan \nEmployee Assistance Program (Lyra)\nModern and convenient office that you can virtually visit here - https://goo.gl/maps/CLteHfYcdYMbdESq6 \nTrainings & possibility to develop skills in a wide international environment \nWant to know even more about us? Take a look at our career page: https://workwithus.circlek.com/global/en/businesscentrewarsaw\nInterested?\nWe encourage you to apply.\nWe know great companies are built from within, by great people like you. Come grow with us! We´re looking forward to your application.\nWhen working with us you can depend upon it that you will not be judged on the grounds of race, national origin, gender, sexual orientation, disability, age, or other legally protected status. Oppositely - we believe that our diverse and inclusive culture helps us create an amazing atmosphere where everybody feels welcome.\nCheck who we are here: https://youtu.be/td-QGnNnvW0\nWe hereby inform that in the company Circle K Business Centre Poland sp. z o.o. with registered office in Warsaw an Internal Notification and Follow-up Actions Procedures applies. The document describes rules for reporting violations of law by whistleblowers. 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