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AI Architect
This a Full Remote job, the offer is available from: Europe, European Union, EMEA, Slovakia, Czechia
We are looking for a senior, hands-on cACV Machine Learning Forward Deployed Engineer to design and deliver production-ready machine learning and generative AI solutions. Working closely with Sales, Partner, and Customer Success teams, the role translates high-value go-to-market challenges into dependable AI systems that accelerate customer adoption and cloud consumption. The role combines machine learning engineering, AI model tuning, and AI harness architecture, with a focus on measurable business impact and consumed ACV (cACV) growth. Responsibilities Design, train, and deploy models for prediction, classification, ranking, and recommendation use cases, such as pipeline scoring, churn and expansion propensity, and opportunity prioritization Partner with data engineering to source, clean, and structure enterprise data. Build reliable feature pipelines using Databricks and SAP Datasphere, with attention to data quality, lineage, and reproducibility Design and maintain conceptual, logical, and physical data models - including dimensional, star/snowflake, Data Vault, and lakehouse medallion approaches - to unify SAP and non-SAP go-to-market data Fine-tune foundation and task-specific models using appropriate techniques, including supervised fine-tuning, LoRA/QLoRA, adapters, RLHF, and DPO. Benchmark commercial and open-weight models and recommend options based on quality, cost, risk, and compliance Design RAG pipelines, embedding strategies, chunking, and reranking. Develop, version, and manage prompts and system instructions Improve latency, throughput, and inference cost through model selection, distillation, quantization, caching, and model routing Build the orchestration, tool and function calling, memory, context management, multi-step workflows, and fallback logic needed to make AI systems reliable in production Create automated evaluation suites using golden datasets, LLM-as-judge, regression tests, and human review. Implement validation, grounding checks, hallucination detection, PII handling, and policy enforcement Instrument AI workflows with tracing, logging, and quality dashboards; monitor drift, failure modes, and user feedback Connect AI solutions to CRM, ERP, collaboration, and content systems using APIs, connectors, and secure data access patterns Tie solutions to outcomes such as seller productivity, reduced cycle time, improved win rates, customer adoption, cloud consumption, and cACV contribution. Report results to leadership Requirements Experience designing, building, and deploying production machine learning and generative AI systems Strong knowledge of machine learning approaches for prediction, classification, ranking, and recommendation Experience with enterprise data engineering, feature pipelines, and data modeling, including lakehouse and dimensional modeling patterns Familiarity with Databricks and SAP Datasphere, and the ability to work with SAP and non-SAP go-to-market data Practical experience with model fine-tuning, RAG, prompt engineering, model evaluation, and inference optimization Ability to architect production AI workflows, including orchestration, tool use, context management, fallback logic, and guardrails Experience integrating AI applications with enterprise systems through APIs, connectors, and secure data access Ability to partner with business stakeholders, translate operational challenges into technical solutions, and connect delivery to measurable outcomes Strong focus on reliability, data quality, security, responsible AI, and production observabilityThis offer from "EPAM Systems" has been enriched by Jobgether.com and got a 0% flex score.
