Machine Learning Engineer - Identity Risk & Knowledge Graph
We are seeking a technically deep Machine Learning Engineer to join our Security & Identity team. This role is ideal for someone who enjoys working at the intersection of graph data modelling and applied machine learning.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
ML Engineer - 100% Remote
Location: 100% Remote
Duration: 6+ Months
Budget- 1.5 LPM
Experience: 5+ yrs
Job Title : ML Engineer - Identity Risk & Knowledge Graph
Budget- 1.5 LPM
Location: Remote/Hybrid
Experience: 5-8+ years overall, 3+ years in ML, 1-2+ years with graph analytics / graph DB
We are seeking a technically deep Machine Learning Engineer to join our Security & Identity team. This role is ideal for someone who enjoys working at the intersection of graph data modelling and applied machine learning.
What you’ll do:
- Design and implement graph-based models to represent complex identity and access relationships.
- Develop and deploy ML-driven anomaly detection capabilities.
- Build and optimize cloud-native data pipelines to support large-scale analytics in enterprise environments.
Required Skills & Experience
- Python, pandas, scikit-learn with 3+ years of hands-on experience
- Strong experience with AWS (S3, Glue, Lambda, Step Functions) - 2-3+ years
- Hands-on experience with Neo4j and Cypher, including exposure to Neo4j GDS
(minimum 1 year or 1+ major project) - Working experience of Graph Neural Networks (GNNs)
- 2+ years of experience in unsupervised ML and anomaly detection
- Proven experience building and deploying ML services / APIs
- Experience integrating ML solutions with LLM platforms (Bedrock, SageMaker, OpenAI) - at least one project
Nice to Have
- Exposure to IAM, identity, or security analytics
- Knowledge of SAP Security / SoD concepts
- Familiarity with Active Directory / Azure AD
- Exposure to SAP SuccessFactors