SAP Data Architect with AI experience

Compugra Systems Inc

Website: compugra.com
Job details:

What You’ll Do:

• Build and deploy LLM-powered agents for enterprise data validation — reading specs, reasoning about business rules, identifying failure modes, and generating structured outputs
• Design and own evaluation frameworks: automated test suites, LLM-as-judge pipelines, regression detection, and benchmarks that track whether our agents are improving
• Build RAG pipelines that work reliably on real enterprise data — messy schemas, inconsistent formats, mixed structured and unstructured content
• Integrate AI systems with enterprise infrastructure (SAP, Snowflake, Databricks, Postgres, REST APIs) with attention to latency, data residency, and compliance
• Design agentic workflows with tool use, multi-step reasoning, and deterministic guardrails
• Build observability tooling: trace agent reasoning, track output reliability, and detect hallucinations or drift in production
• Work directly with FDSEs to understand real deployment failures and translate them into system improvements

The Stack

• Languages: Python (primary), Go, Node.js
• AI/ML: LLMs (Claude, GPT-4, Command R+), RAG, vector databases, embeddings, fine-tuning
• Evaluation: LLM-as-judge, automated eval pipelines, custom benchmarks
• Data: Snowflake, Databricks, Postgres
• Infra: containers, Kubernetes / ECS / Cloud Run
• Tools: LangChain, LlamaIndex, OpenAI / Anthropic APIs, LangSmith

Who You Are:

• Production builder: you’ve shipped LLM-powered features real users depend on and debugged them when they broke
• LLM practitioner: you understand hallucinations, retrieval failures, context limits, and what it takes to make agents deterministic enough for enterprise use
• Systems thinker: you design for latency, failure modes, retry logic, and observability before features
• Enterprise-aware: data residency, compliance, audit trails, and deterministic guardrails are first-class design constraints for you

Background That Maps Well:

• 3+ years in AI/ML or backend engineering with strong AI exposure
• Hands-on production experience with LLM APIs (Anthropic, OpenAI, Cohere)
• Experience designing evaluation frameworks: automated evals, regression tests, or LLM-as-judge pipelines
• Strong Python; experience with LangChain, LlamaIndex, or similar agentic frameworks
• Familiarity with RAG architectures: chunking, embedding models, vector DBs, retrieval quality

Nice to Have:

• Familiarity with SAP systems and enterprise data structures
• Experience with fine-tuning or RLHF on domain-specific datasets
• Exposure to compliance-heavy industries: healthcare, financial services, or manufacturing

Click on Apply to know more.