{"id":3454553,"url":"https://alion.io/job/doit-senior-aiml-engineer-python-llm-rag","title":"Senior AI/ML Engineer (Python, LLM, RAG)","company":{"id":1778343,"name":"DOIT","domain":"doit.software","url":"https://alion.io/company/doit-software","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":{"label":"ET","utc_offset_min":-5,"utc_offset_max":-5},"hiring_geo_confidence":"inferred","locations":["Ukraine"],"countries":["UA"],"hiring_countries":["US","UA"],"hiring_countries_total":2,"salary":null,"salary_estimate":{"min_usd":27000,"max_usd":72000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2365},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":".NET","optional":false},{"name":"Agile","optional":false},{"name":"Angular","optional":false},{"name":"Anthropic","optional":false},{"name":"C#","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Claude Code","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"Prompt Caching","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Scrum","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TensorFlow","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Docker","optional":true},{"name":"JavaScript","optional":true},{"name":"MLFlow","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"OpenAI Agents SDK","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-10-20T13:34:23Z","employer_posted_date":null,"last_verified_at":"2026-10-20T13:34:23Z","board_verified":false,"closed_at":null,"days_open":10,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":10},"description":"DOIT Software is looking for an AI/ML Engineer to join the engineering team of a B2B SaaS product company and build machine learning and AI features into its platform.\n\nAbout the Client\n\nOur client is a SaaS product company working in analyst relations (AR) and B2B influencer relations. Its platform helps technology vendors and B2B companies manage, track, and measure their relationships with industry analysts who influence buyers in the market.\n\nThe product combines a specialized CRM with a searchable database of analysts and influencers, analytics and reporting dashboards (share of voice, sentiment, competitive intelligence), and an analyst-facing self-service portal.\n\nAbout the Role\n\nYou will design Python-based models and services, integrate large language models (Anthropic’s Claude), and ship them alongside the client’s software engineering team.\n\nThe platform runs on Angular, C# (.NET), SQL Server and MongoDB. You will not be the primary developer on that stack, but you will work in it daily: pulling data from it, exposing your models to it through APIs, and pairing with engineers to get AI features into production.\n\nYou will report to the VP of Engineering. This is a full-time, fully remote position that requires at least 3-4 hours of daily overlap with US Eastern Time for standups and pairing.\n\nWhat You’ll Do\n\nMachine learning and data science\n\nDesign, train, evaluate and deploy ML models for classification, clustering, recommendation, NLP and forecasting use cases.\n\nExplore, clean and engineer features from product data stored in SQL Server and MongoDB.\n\nDefine metrics, run experiments and report results clearly to engineering and product stakeholders.\n\nMonitor models in production for drift, accuracy and cost, and retrain as needed.\n\nAI and Claude integration\n\nBuild LLM-powered features using the Claude API: prompt design, tool use, structured outputs and retrieval-augmented generation (RAG).\n\nDevelop evaluation sets and automated tests to measure LLM output quality, accuracy and safety.\n\nOptimize LLM usage for latency and cost (prompt caching, model selection, batching).\n\nUse AI coding assistants such as Claude Code to speed up your own development work.\n\nWorking with the product engineering team\n\nPackage models and AI logic as Python services and REST APIs that the C# (.NET) backend calls.\n\nWork with Angular developers to shape how AI features appear and behave in the UI.\n\nWrite efficient SQL Server queries and MongoDB aggregations, and help design schemas for the data that ML features need.\n\nTake part in code reviews, sprint planning and standups, following the team’s Git, CI/CD and testing practices.\n\nDocument models, data pipelines and APIs so other engineers can support them.\n\nRequired Qualifications\n\n3–5 years of professional experience in data science, ML engineering or a similar role.\n\nBachelor’s degree in Computer Science, Data Science, Statistics, Mathematics or a related field, or equivalent experience.\n\nStrong Python skills, including pandas, NumPy and scikit-learn.\n\nHands-on experience with at least one deep learning framework (PyTorch or TensorFlow).\n\nSolid understanding of ML fundamentals: supervised and unsupervised learning, feature engineering, model evaluation and avoiding overfitting.\n\nPractical experience building with LLM APIs, ideally Anthropic Claude: prompt engineering, tool use, RAG and output evaluation.\n\nExperience building and consuming REST APIs in Python (FastAPI or Flask).\n\nWorking knowledge of SQL (SQL Server preferred) and a NoSQL document database (MongoDB preferred).\n\nComfortable with Git, code reviews and Agile/Scrum workflows.\n\nClear written and spoken English (Upper-Intermediate or higher) for daily collaboration with a distributed team.\n\nProactive written communication: status updates, clear questions and documented decisions.\n\nOwnership of features from prototype to production, not just notebooks.\n\nCuriosity about new AI capabilities and the judgment to know when a simpler approach is better.\n\nNice to Have\n\nExperience with vector databases or embedding search (e.g., MongoDB Atlas Vector Search, pgvector, Pinecone).\n\nFamiliarity with agent frameworks, the Model Context Protocol (MCP) or the Claude Agent SDK.\n\nReading-level familiarity with C# / .NET and Angular / TypeScript, enough to trace how your services are called.\n\nMLOps experience: Docker, CI/CD pipelines, model versioning (MLflow or similar) and cloud deployment (Azure or AWS).\n\nPrior work on a multi-tenant B2B SaaS product.\n\nNLP work on unstructured business text such as documents, emails or research content.\n\nUnderstanding of data privacy and security practices for customer data used in AI features.\n\nRecruitment Process\n\nPre-screen call with a Recruitment Manager.\n\nTechnical interview with DOIT Software.\n\nOnline call with the 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