Software Development Senior Specialist
- Compensation
- Not disclosed
- Location
- Bengalore, KA, IN
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Not specified
- Posted
Skills
Must have
- 5+ years of progressive experience in Site Reliability Engineering, Machine Learning Engineering, or Data Platform Engineering operating at enterprise scale.
- Proven track record of shipping production-grade ML models and data pipelines rather than purely experimental or research-focused workflows.
- Strong software engineering fundamentals in Python and production ML frameworks (e.g., scikit-learn, XGBoost, pandas/numpy, and modern time-series/clustering libraries).
- Deep data engineering experience: designing canonical schemas, building streaming/batch ingestion pipelines, and enforcing data hygiene across messy, high-volume telemetry sources.
- Practical understanding of core SRE principles (SLIs/SLOs, error budgets, incident lifecycle management, distributed observability).
- Strong technical communication skills with a proven ability to lead cross-functional architecture reviews across platform, operations, and security stakeholders.
Nice to have
- Hands-on experience applying Graph ML or graph analytics to infrastructure topology, service maps, or dependency networks.
- Demonstrated experience building production LLM-assisted workflows, including tool calling, agentic orchestration frameworks, vector databases, and formal evaluation benches.
- Hands-on expertise with enterprise observability pipelines and log aggregators (e.g., OpenTelemetry, Prometheus, Kafka, Vector, ClickHouse, or major cloud-native logging ecosystems).
- Familiarity with enterprise AI governance, model risk management, auditability, and data privacy requirements.
- Experience managing containerized workloads and operational pipelines on Kubernetes.
Full description
Req ID: 395187
NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.
We are currently seeking a Software Development Senior Specialist to join our team in Bengalore, Karnātaka (IN-KA), India (IN).
Senior Site Reliability & AIOps Platform Engineer (Applied ML & LLM)Role Overview
As a Senior Site Reliability & AIOps Platform Engineer, you will design, build, and operationalize the machine learning and data foundations that transform our enterprise infrastructure from reactive, alert-driven operations into predictive, self-healing platforms. You will operate at the intersection of distributed systems reliability, data engineering, and applied artificial intelligence - replacing operational toil and alert fatigue with topology-aware correlation, statistical anomaly detection, and agentic triage workflows. In this role, you will define the canonical telemetry models that unify operational signals across multi-cloud environments, deploy ML models that eliminate noise at enterprise scale, and lead the architecture of LLM-powered incident intelligence. You will partner with platform and infrastructure teams to accelerate Mean Time to Resolution (MTTR), enforce strict Service Level Objectives (SLOs), and scale closed-loop automation under rigorous enterprise governance.
Key ResponsibilitiesTelemetry Foundations & Canonical Operational Schemas
- Architect and standardize canonical data models that unify multi-source alerts, incident tickets, infrastructure topology, maintenance windows, and deployment events into a consistent operational fabric.
- Design robust ingestion pipelines and streaming connectors across heterogeneous infrastructure stacks, networks, hyper-scalers, and containerized platforms.
- Engineer automated data extraction pipelines to mine structured outcome labels (e.g., automated mitigations, recurring anomalies, human interventions) from historical operational data to train supervised models.
- Establish data contracts, quality controls, latency SLAs, and automated data-protection and masking guardrails across all operational feature stores and vector pipelines.
Applied Machine Learning & Predictive Incident Intelligence
- Design and deploy supervised classifiers for dynamic alert noise scoring, change-risk impact forecasting, and event suppression.
- Implement unsupervised clustering, pattern mining, and graph-based topology correlation over complex dependency graphs to intercept alert cascades and multi-hop cascading failures.
- Build statistical and time-series anomaly detection models (e.g., forecasting, isolation algorithms) to identify performance degradation and drive proactive capacity planning across compute, storage, and network layers.