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Accenture

Advises businesses and runs their technology

Deployment Lead

Enterprise ApplicationsConfirmed live in the last 24 hours
Full-timeMumbai, Maharashtra, IndiaNo salary listedIn-person

About the Job

Requirements

  • A minimum of 7.5 years of experience is required.
  • 15 years of full-time education is required.
  • Strong hands-on expertise in SAP SuccessFactors technical integrations, including design, build, troubleshooting, and operation.
  • Solid understanding of enterprise integration fundamentals, including APIs, payload formats, transformations, scheduling, eventing, secure transport, and monitoring.
  • Hands-on capability with LLM-integrated workflows, including tool-calling orchestration, retrieval-grounding, prompts/templates, and evaluation harnesses.
  • Engineering discipline, including clean implementation, automated testing, a CI/CD mindset, and production reliability practices.

Responsibilities

  • Plan and lead execution of a comprehensive deployment plan, including work planning, scheduling, budgeting, metrics, training, pilots, and resources.
  • Collaborate with project teams to manage interdependencies, align deployment-related activities, and monitor and control progress through the deployment plan.
  • Design and implement end-to-end SuccessFactors integrations across HR domains, including employee lifecycle, organizational data, onboarding, learning, recruiting, and payroll-adjacent flows.
  • Build and support integrations using API-based, file-based, event-triggered, and scheduled/batch approaches aligned to enterprise integration patterns.
  • Define integration blueprints covering interface contracts, payload schemas, transformations, error handling, retries, and reconciliation approaches.
  • Engineer integrations between SuccessFactors and enterprise systems, including ERP, payroll, identity and access, finance, time systems, third-party HR applications, and data platforms.
  • Build middleware orchestrations using routing, transformation, enrichment, correlation IDs, secure connectivity, and throttling.
  • Enable secure transport patterns such as SFTP and HTTPS, credential management, certificate handling, and environment promotion across landscapes.
  • Implement event-driven integration approaches for employee and organizational changes, keeping systems consistent with minimal latency and controlled data distribution.
  • Establish canonical data modeling and semantic mapping practices to reduce duplication and point-to-point sprawl and improve maintainability.
  • Drive data minimization and privacy-aware integration design by sharing only necessary data where needed.
  • Build integration agents that interpret integration intent, generate and validate mappings, and propose transformation logic using tool-based orchestration with schema tools, metadata tools, and test harnesses.
  • Implement retrieval-grounded integration assistance that retrieves interface specifications, mapping rules, runbooks, and incident patterns to produce grounded outputs.
  • Create in-the-run integration operations copilots for conversational troubleshooting, guided remediation, automated log summarization, and impact analysis, backed by verifiable evidence.
  • Build automated integration testing strategies, including contract tests, schema validation, payload regression suites, replay testing, and environment parity checks.
  • Implement offline and online evaluation loops for AI behaviors, including golden test sets for mappings and transformations, hallucination checks, grounding rates, and safety regressions.
  • Define release gates for integration changes and AI prompt/tool updates to prevent silent production degradation.
  • Establish observability for integrations and AI components, including structured logs, traces, metrics, correlation, and alerting across the integration chain.
  • Engineer robust failure-mode handling, including retries, fallbacks, circuit breakers, dead-letter queues/error queues, and reconciliation dashboards.
  • Build AI-assisted incident response patterns for triage, root-cause clustering, and remediation recommendations with strict access controls and human-in-the-loop confirmations.
  • Enforce enterprise governance for data privacy, authorization boundaries, and least-privilege access, especially for sensitive HR attributes.
  • Implement responsible AI guardrails for integration agents, including tool permissions, action boundaries, redaction rules, and approval workflows for high-risk operations.
  • Maintain auditability across integration actions and AI-generated decisions, including who, what, why, and when, suitable for compliance needs.

Desired Qualifications

  • Experience with integration middleware and iFlow-style orchestration patterns.
  • Familiarity with HR domains, including employee master data, organizational structures, time/absence, onboarding, learning, recruiting, and payroll-adjacent data flows.
  • Data modeling practices for canonical HR entities and semantic consistency across systems.
  • Experience with secrets management, certificate lifecycle management, and enterprise security controls in integration landscapes.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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