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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 observability