Job Description
Senior AI Systems Engineer
We are looking for a Senior AI Systems Engineer to design, build, and scale AI-driven operational intelligence and agentic systems for large-scale enterprise and cloud environments.
You will apply machine learning, predictive modelling, and agentic workflows to real operational problems - anomaly detection, root cause analysis, predictive remediation, and intelligent automation moving Operations from reactive to predictive and semi-autonomous.
This is a hands-on engineering role. You will own the full lifecycle: data, models, agents, services, deployment.
Core Skills: Machine Learning, AI Development, Statistics, Data-Driven Insights, Prediction Models, Generative AI, Agentic AI, Python, Cloud-Native Engineering
What you'll build
- Prediction and anomaly-detection models
- Agentic workflows spanning Detection - Triage - RCA - Recommendation - Action with human-in-the-loop guardrails
- RAG-based retrieval systems over runbooks, historical incidents, knowledge bases, and resolution patterns
- Production AI services and APIs deployed on cloud infrastructure, with observability, evaluation, and drift monitoring built in
- Data pipelines that turn noisy, unstructured operational signal into structured, model-ready features
What you bring
Must Have
- 4-11 years of hands-on engineering experience, with a strong backend / distributed-systems foundation
- Hands-on Machine Learning and AI development experience - building, training, and deploying prediction models (supervised, unsupervised, time-series) in production
- Solid grounding in statistics - distributions, hypothesis testing, regression, correlation analysis
- Ability to generate data-driven insights from messy operational data - logs, metrics, traces, ticketing data and turn noise into signal
- Hands-on GenAI and LLM engineering - RAG, prompt engineering, embeddings, vector stores, evaluation
- Demonstrable agentic AI workflow experience - multi-step orchestration, tool/function calling, agent design patterns, guardrails
- Strong Python - clean, production-grade code; APIs, microservices, async, testing
- Cloud-native engineering - AWS, Azure, or GCP; Docker, Kubernetes, CI/CD
- Systems thinking - you design workflows and feedback loops, not just models or prompts
Strongly Preferred
- Exposure to AIOps, SRE, or production support environments
- Experience with observability stacks - Prometheus, Grafana, ELK, Datadog, Splunk, or similar
- Experience with incident and ticketing systems - ServiceNow, Jira or equivalent
- Familiarity with ML/AI frameworks - scikit-learn, PyTorch/TensorFlow, LangChain, LlamaIndex, LangGraph, or comparable
- Experience deploying AI systems with evaluation, drift detection, and human-in-the-loop patterns
Good to Have
- Exposure to SAP HANA, SAP Basis, or other enterprise database systems
- Familiarity with SAP BTP - AI Core, Generative AI Hub, AI Launchpad, workflow services
- Awareness of ITIL processes - Incident, Problem, Alert, Change Management
- Experience with SAP FRUN, Solution Manager, or SAP Analytics Cloud
Education
Bachelor's or Master's in Computer Science, Engineering, Statistics, Applied Mathematics, or a related field.
Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Job Classification
Industry: IT Services & Consulting
Functional Area / Department: Engineering - Software & QA
Role Category: Software Development
Role: Data Platform Engineer
Employement Type: Full time
Contact Details:
Company: SAP
Location(s): Bengaluru
Keyskills:
cleaning
cd
kubernetes
continuous integration
python
hypothesis testing
software testing
production
ci/cd
engineering
analysis
distribution
machine learning
artificial intelligence
microservices
docker
tensorflow
grafana
pytorch
api
aws
statistics