{"id":3170462,"url":"https://alion.io/job/globant-ai-machine-learning-engineer","title":"AI / Machine Learning Engineer","company":{"id":61174,"name":"Globant","domain":"globant.com","url":"https://alion.io/company/globant","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"C","score":55,"open_postings":79,"ghost_share":0.747,"stale_share":0,"repost_share":0.013,"time_to_fill_p50_days":50,"computed_at":"2026-10-26T05:57:25Z"}},"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":["United 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production pricing agents on NEO, owning them from prototype through production, as part of the firm's federated portfolio of production agents.\nBuild retrieval that holds up in production: Graph RAG combining knowledge-graph traversal over products, rate cards, clients, and pricing policy with vector search, plus chunking, ranking, and grounding strategies that keep price explanations accurate and auditable.\nDesign agent memory: episodic and semantic memory organized as memory nodes, with recall, summarization, and decay policies tuned per use case, so an agent remembers the history of a negotiation without leaking it across clients.\nOwn organizational context management - assembling entitlement-, lineage-, and tenant-aware context so each agent reasons over only the pricing data it is allowed to see.\nBuild and productionize ML model training pipelines for price optimization, elasticity, win-probability, and deal-scoring models using MLOps best practices, including feature engineering over billing, transaction, and deal history data on Databricks.\nBuild batch and online inference for those models, meeting the latency a seller conversation requires while keeping every scored recommendation reproducible.\nOwn model validation in a revenue-sensitive setting: back-testing against realized prices, challenger models, uplift and elasticity measurement, and monitoring for drift, margin erosion, and revenue leakage in production.\nCompose multi-agent workflows using A2A, and integrate tools and data through MCP servers (pricing engine, rate card store, CRM, billing and revenue data, Databricks, Snowflake, Kubernetes, Confluence, Bitbucket, Splunk).\nBuild and run evals: task-level and end-to-end agent evaluations, regression suites, LLM-as-judge, numerical-correctness and explanation-faithfulness checks, and quality and safety gating before release.\nProduce the model documentation, test evidence, and controls the firm's model risk review requires for a model that influences client pricing, and keep that evidence current as models are retrained.\nDeploy and operate solutions on public cloud (AWS and/or Azure) with strong SDLC, security, resiliency, and observability practices.\nPartner with pricing, product, finance, and business partners across CIB and Payments to turn use cases into shipped, supported agents.\nRequired Qualifications, Capabilities, and Skills\nMS in Computer Science, Statistics, Mathematics, Machine Learning, or related field (or equivalent experience).\nMinimum 7 years of development experience, with at least 4 years working on AI/ML solutions.\nHands-on experience building LLM-powered or agentic applications in production, including tracing, evaluations, and guardrails.\nHands-on experience taking predictive or optimization models to production and keeping them healthy there, including validation, monitoring, and retraining.\nPractical RAG experience - retrieval quality, embeddings, and vector stores; Graph RAG a strong plus.\nStrong programming skills in Python, with deep knowledge of data structures, algorithms, machine learning, data mining, information retrieval, and statistics.\nKnowledge of Kubernetes (AWS EKS).\nExperience with training models in Databricks and SageMaker.\nExperience working with MLflow.\nExpert knowledge of at least one of: AWS, Azure, Kubernetes.\nKnowledge of data management and data model design; real-time processing using both SQL (e.g., Postgres) and NoSQL stores (e.g., OpenSearch, Redis).\nExcellent communication skills with the ability to partner effectively with senior technical and business stakeholders.\nExperience with pricing, revenue management, elasticity, or demand forecasting models in a production setting.\nExperience with mathematical optimization (linear and mixed-integer programming, e.g., OR-Tools or Gurobi) and with constrained recommendation problems.\nExperience with causal inference, uplift modelling, or experiment design where randomization is limited.\nExperience delivering models through model risk management or equivalent regulatory review.\nExperience working in the financial or payments domain at a large institution (Investment Banking, Markets, Securities Services, or adjacent), or with commercial card, treasury services, or billing and revenue systems.\nExperience with agent frameworks or runtimes, A2A, or MCP.\nAgent memory design (memory nodes, episodic/semantic memory) and organizational context management.\nKnowledge graphs and graph databases used for retrieval.\nUnderstanding of LLM fine-tuning and small language model inference.\nAbility to develop full-stack products using modern JavaScript/TypeScript frameworks (e.g., Next.js, Svelte) for agent UIs (AG-UI / NEO UI SDK).\nKnowledge of high-performance languages such as Go or 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