Software Development Senior Specialist

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 Responsibilities

Telemetry 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.
• Drive the end-to-end MLOps lifecycle for operations—building reproducible training workflows, validation suites, concept/data drift monitoring, and continuous retraining schedules.

Agentic Operations, LLM Tooling & Contextual Enrichment

• Architect intelligent enrichment workflows utilizing LLMs and Retrieval-Augmented Generation (RAG) to dynamically synthesize tickets with runbooks, historical incident post-mortems, and live architectural state.
• Develop agentic incident response tools leveraging standard interface protocols (e.g., Model Context Protocol / tool-calling frameworks) and multi-agent coordination to automate first-pass root-cause analysis on complex, multi-signal failures.
• Establish empirical evaluation harnesses (e.g., programmatic evals, LLM-as-Judge, deterministic regression suites) to measure model quality, hallucination risk, and accuracy before operational deployment.
• Expand closed-loop automated remediation by identifying recurring failure signatures and safely mapping them into deterministic execution engines with confidence-scored validation gates.

Cloud Platform Reliability & AI Governance

• Embed reliability engineering principles into AI pipelines, treating ML services and agentic workflows as critical tier-0 platform components governed by SLIs, SLOs, and error budgets.
• Lead the operational rollout of AI tools through staged governance tiers (development sandbox, supervised review, human-in-the-loop validation, bounded autonomous execution).
• Drive post-incident analysis on novel outages, utilizing post-mortem telemetry to continuously refine correlation algorithms and suppression policies.

Basic Qualifications

• 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.

Preferred Qualifications

• 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.

About NTT DATA

NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.

Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client’s needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com, @nttdatafed.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form, .

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