Senior Integration & Automation Engineer
Rubrik · Bangalore · 10+ yrs experience · Posted 2026-06-17
Tech stack: AWS, Azure, Kafka, SQL
About the role
About the role
The
Senior Integration, API & Automation Engineer
is a hands-on technical lead role responsible for shaping and executing Rubrik’s
enterprise API, integration, and AI‑driven automation strategy
.
Broadly, the role will focus on
designing, building, and governing secure, scalable APIs and integrations
between our core business platforms and third‑party systems (e.g., ERP, HCM, CRM, data platforms), while mentoring engineers and driving best practices across the organization. In addition, this role will help us
embed AI capabilities
(LLMs, Claude, decisioning services, intelligent workflows) into APIs and business processes to improve reliability, productivity, and insight.
You will work closely with senior business stakeholders, application owners, security, data, and IT teams to define
API‑first, AI‑aware integration patterns and standards
that deliver robust, secure, real‑time and batch integrations using
REST APIs, webhooks, messaging, file‑based interfaces
, and AI services.
This role will primarily leverage
MuleSoft Anypoint Platform (or similar iPaaS)
for API‑led connectivity and sits within the
Enterprise Integrations & Intelligent Automations
team as a key technical authority.
What you'll do
Own the API and integration architecture and strategy
for assigned domains, defining
API‑led patterns
(Experience, Process, and System APIs), reference architectures, and standards for how systems integrate across the enterprise.
Design, build, and evolve RESTful APIs
that expose well‑designed resources and operations to internal and external consumers, with clear contracts, documentation, and SLAs.
Lead implementation of integrations
between SaaS and on‑prem applications (e.g.,
NetSuite, Workday, Salesforce, Snowflake, ServiceNow
), Finance, HR, Security, and GTM platforms, ensuring solutions are scalable, secure, resilient, and observable.
Create data integration strategies
for high volumes of data in transit, optimizing for performance, reliability, and cost (e.g., streaming vs. batch, push vs. pull APIs, caching, backoff strategies).
Define and enforce API governance
, including:
Provide technical leadership
to integration and API engineers, including solutioning, design reviews, code reviews, and mentoring of junior and contract engineers.
Implement and manage REST/SOAP APIs, webhooks, and messaging interfaces
from conceptual design through development, performance testing, deployment, and lifecycle management via an API gateway or iPaaS (e.g., MuleSoft Anypoint Platform).
Architect robust data integrations
for both real‑time and scheduled (batch) workloads using APIs, messaging, sFTP, and file‑based exchanges (
JSON, XML, CSV
), with well‑defined contracts, SLAs, and monitoring.
Define non‑functional requirements (NFRs) and SLAs
for APIs and integrations—availability, latency, throughput, error budgets, RTO/RPO—and ensure designs and implementations meet or exceed them.
Drive observability and reliability
for APIs and integrations by standardizing logging, tracing, metrics, dashboards, and alerting, and by creating operational runbooks for incident response and post‑incident reviews.
AI & Intelligent Automation Responsibilities
Identify and prioritize AI opportunities
within API and integration flows (e.g., data enrichment, anomaly detection, routing, summarization, classification, intent extraction).
Define safe and governed AI usage
within APIs and integrations, including:
Clear
input/output contracts
for AI services
Guardrails (prompt design, validation, constraints)
Handling of sensitive data (PII/financial/HR data) in prompts and responses
Monitoring for model performance and drift in production.
Integrate with AI platforms and services
(internal or external), including model APIs, vector stores, and feature stores, using strong API design and security practices.
Stakeholder, Security, and Platform Responsibilities
Partner with security, compliance, and audit teams
to ensure integrations, APIs, and AI services meet security, privacy, and regulatory requirements (e.g., SOX‑sensitive data flows, access control, encryption, audit trails).
Lead technical discovery and solution design
for new API, integration, and AI‑automation initiatives, working with product managers, application owners, and business stakeholders to shape requirements and translate them into well‑defined technical designs.
Own vendor and platform technical relationships
for integration technologies and key SaaS systems; understand vendor APIs and change roadmaps, anticipate the impact of deprecations, and coordinate upgrades and migrations.
Oversee CI/CD practices for APIs, integrations, and AI services
, including branching and release strategies, automated testing (unit, integration, regression), and environment configuration management for Dev/QA/UAT/Prod.
Champion reuse and standardization
by building and curating shared assets such as:
Generic APIs (e.g., email, JIRA, logging, AI utility services)
Common connectors and API client libraries
Templates and integration catalogs
and ensuring they are adopted broadly.
Participate in and improve KTLO/on‑call rotations
for critical APIs, integrations, and AI services, using incident data to drive structural improvements, technical debt reduction, and increased reliability.
Communicate clearly with technical and non‑technical audiences
, producing architecture diagrams, decision records, and documentation that make complex API, integration, and AI solutions easy to understand, operate, and evolve.
Experience you'll need
Education & Experience
Bachelor’s or Master’s degree in
Computer Science, Information Systems, Engineering
, or equivalent practical experience.
10+ years
of overall software/integration engineering experience, with
8+ years focused on API and enterprise integrations
.
4+ years
in a senior/lead/architect capacity, owning API and integration designs, patterns, and technical decisions for complex cross‑system and enterprise application solutions.
Experience
designing and delivering AI‑enabled features or workflows
as part of production systems (e.g., integrating with LLM or ML APIs, AI‑based decisioning, or intelligent automation).
Technical Skills – API Engineering
Deep expertise in
RESTful API design and implementation
, including:
Resource modeling, URI design, and standard HTTP methods
API‑first/contract‑first design (RAML/OpenAPI)
Pagination, filtering, sorting, and partial responses
Versioning strategies (URL, header, content negotiation)
Idempotency and robust error handling patterns.
Strong experience building
secure, resilient, and scalable APIs
, including:
API gateways (e.g., MuleSoft, Apigee, Kong, AWS API Gateway)
Rate limiting, throttling, caching, and circuit‑breaker patterns
Timeout, retry, and backoff strategies.
Solid understanding of
webhooks and event‑driven APIs
, including subscription management, signature validation, replay protection, and idempotent consumers.
Hands-on experience with
API client development
and integration patterns (SDKs, service‑to‑service communication, backend‑for‑frontend, composite APIs).
Technical Skills – Integration & Platforms
Strong experience with
MuleSoft Anypoint Platform (or equivalent iPaaS)
, including API Manager, Runtime Manager/CloudHub, Anypoint Exchange, and design/deployment best practices.
Solid understanding of
SOAP services, messaging patterns
(pub/sub, event‑driven integrations), and when to apply each.
Proficiency with
integration data formats and transformation
(JSON, XML, CSV, mapping, enrichment, aggregation, canonical models).
Strong
database and data modeling
skills, including SQL and working with relational databases (e.g., Postgres, SQL Server, Oracle) in integration scenarios.
Experience designing and implementing
secure integrations
, including:
OAuth 2.0/OpenID Connect
API keys and mutual TLS
Role/permission models and fine‑grained authorization
Secrets management (e.g., HashiCorp Vault, cloud KMS).
Hands‑on experience with
CI/CD pipelines and Git‑based workflows
, including automated testing, static analysis, deployment automation, and environment configuration for API and integration services.
Demonstrated experience integrating with several of the following:
NetSuite, Workday, Salesforce, Snowflake, ServiceNow, Coupa, DocuSign, ChromeRiver, Marketo, identity/IAM platforms, external APIs, and internal APIs
.
Practical experience working with
AI/ML or LLM APIs
(e.g., OpenAI, Azure OpenAI, Vertex AI, Bedrock, or internal model APIs), including:
Designing API requests and responses for AI‑backed features
Prompt engineering and response validation
Handling rate limits, retries, timeouts, and partial failures within integration flows.
Familiarity with
event‑driven and streaming architectures
(e.g., Kafka, EventBridge, Event Hubs) and how to combine them with AI services for near real‑time decisioning and automation.
Soft Skills
Proven ability to
lead without direct authority
, influencing cross‑functional teams and driving technical decisions through clarity and data rather than hierarchy.
Strong
architectural thinking and problem‑solving skills
, including the ability to decompose complex business processes into robust API, integration, and AI‑enabled designs.
Excellent
communication and documentation skills
, comfortable presenting solutions and trade‑offs to engineers, managers, and senior business stakeholders.
Experience working in
agile environments
, collaborating closely with product owners, scrum teams, and distributed stakeholders across time zones.
Bias toward
automation, standardization, and continuous improvement
, with a pragmatic approach to balancing speed, risk, and long‑term maintainability.
Preferred Qualifications
Professional
MuleSoft certifications
(e.g., MuleSoft Certified Integration Architect, MuleSoft Certified Developer) or equivalent certifications from other integration platforms.
Experience designing and operating
public or partner-facing APIs
with clear productization (API lifecycle, developer experience, SLAs, and commercial considerations).
Experience designing and operating integrations in
regulated or security‑sensitive environments
(e.g., SOX, FedRAMP, customer‑facing APIs) with a strong emphasis on auditability and change control.
Experience with
event‑driven and streaming platforms
(e.g., Kafka, AWS EventBridge, Azure Event Hubs) and their role in modern API and integration architectures.
Exposure to
automation/workflow platforms
(e.g., Zapier, Workato, low‑code tools) and how they complement enterprise integration layers and AI‑based automation.
Experience building and maintaining
API and integration runbooks, catalogs, and governance processes
, and contributing to enterprise architecture forums or design reviews.
Experience with
observability stacks
(e.g., Datadog, Splunk, New Relic, OpenTelemetry) for monitoring APIs, integrations, and AI services in production.
Familiarity with
data and AI governance
concepts (e.g., data classification, lineage, model governance, responsible AI practices) and how they apply within API, integration, and automation patterns.
Qualifications
- Education & Experience
- Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering or equivalent practical experience.
- 10+ years of overall software/integration engineering experience, with 8+ years focused on API and enterprise integrations.
- 4+ years in a senior/lead/architect capacity, owning API and integration designs, patterns, and technical decisions for complex cross‑system and enterprise application solutions.
- Experience designing and delivering AI‑enabled features or workflows as part of production systems (e.g., integrating with LLM or ML APIs, AI‑based decisioning, or intelligent automation).
- API Engineering Deep expertise in RESTful API design and implementation including:
- Resource modeling, URI design, and standard HTTP methods
- API‑first/contract‑first design (RAML/OpenAPI)
- Pagination, filtering, sorting, and partial responses
- Versioning strategies (URL, header, content negotiation)
- Idempotency and robust error handling patterns.
- Strong experience building secure, resilient, and scalable APIs including:
- API gateways (e.g., MuleSoft, Apigee, Kong, AWS API Gateway)
- Rate limiting, throttling, caching, and circuit‑breaker patterns
- Timeout, retry, and backoff strategies.
- Solid understanding of webhooks and event‑driven APIs including subscription management, signature validation, replay protection, and idempotent consumers.
- Hands-on experience with API client development and integration patterns (SDKs, service‑to‑service communication, backend‑for‑frontend, composite APIs).
- Integration & Platforms
- Strong experience with MuleSoft Anypoint Platform (or equivalent iPaaS)
- including API Manager, Runtime Manager/CloudHub, Anypoint Exchange, and design/deployment best practices.
- Solid understanding of SOAP services, messaging patterns
- (pub/sub, event‑driven integrations), and when to apply each.
- Proficiency with integration data formats and transformation
- (JSON, XML, CSV, mapping, enrichment, aggregation, canonical models).
- Strong database and data modeling skills, including SQL and working with relational databases (e.g., Postgres, SQL Server, Oracle) in integration scenarios.
- Experience designing and implementing secure integrations including:
- OAuth 2.0/OpenID Connect
- API keys and mutual TLS
- Role/permission models and fine‑grained authorization
- Secrets management (e.g., HashiCorp Vault, cloud KMS).
- Hands‑on experience with CI/CD pipelines and Git‑based workflows including automated testing, static analysis, deployment automation, and environment configuration for API and integration services.
- Demonstrated experience integrating with several of the following: NetSuite, Workday, Salesforce, Snowflake, ServiceNow, Coupa, DocuSign, ChromeRiver, Marketo, identity/IAM platforms, external APIs, and internal APIs.
- Practical experience working with AI/ML or LLM APIs
- (e.g., OpenAI, Azure OpenAI, Vertex AI, Bedrock, or internal model APIs), including:
- Designing API requests and
Responsibilities
- Own the API and integration architecture and strategy for assigned domains, defining API‑led patterns
- (Experience, Process, and System APIs), reference architectures, and standards for how systems integrate across the enterprise.
- Design, build, and evolve RESTful APIs that expose well‑designed resources and operations to internal and external consumers, with clear contracts, documentation, and SLAs.
- Lead implementation of integrations between SaaS and on‑prem applications (e.g.
- NetSuite, Workday, Salesforce, Snowflake, ServiceNow
- ), Finance, HR, Security, and GTM platforms, ensuring solutions are scalable, secure, resilient, and observable.
- Create data integration strategies for high volumes of data in transit, optimizing for performance, reliability, and cost (e.g., streaming vs. batch, push vs. pull APIs, caching, backoff strategies).
- Define and enforce API governance including:
- Provide technical leadership to integration and API engineers, including solutioning, design reviews, code reviews, and mentoring of junior and contract engineers.
- Implement and manage REST/SOAP APIs, webhooks, and messaging interfaces from conceptual design through development, performance testing, deployment, and lifecycle management via an API gateway or iPaaS (e.g., MuleSoft Anypoint Platform).
- Architect robust data integrations for both real‑time and scheduled (batch) workloads using APIs, messaging, sFTP, and file‑based exchanges ( JSON, XML, CSV), with well‑defined contracts, SLAs, and monitoring.
- Define non‑functional requirements (NFRs) and SLAs for APIs and integrations—availability, latency, throughput, error budgets, RTO/RPO—and ensure designs and implementations meet or exceed them.
- Drive observability and reliability for APIs and integrations by standardizing logging, tracing, metrics, dashboards, and alerting, and by creating operational runbooks for incident response and post‑incident reviews.
- AI & Intelligent Automation Responsibilities
- Identify and prioritize AI opportunities within API and integration flows (e.g., data enrichment, anomaly detection, routing, summarization, classification, intent extraction).
- Define safe and governed AI usage within APIs and integrations, including: Clear input/output contracts for AI services
- Guardrails (prompt design, validation, constraints)
- Handling of sensitive data (PII/financial/HR data) in prompts and responses
- Monitoring for model performance and drift in production.
- Integrate with AI platforms and services
- (internal or external), including model APIs, vector stores, and feature stores, using strong API design and security practices.
- Stakeholder, Security, and Platform Responsibilities
- Partner with security, compliance, and audit teams to ensure integrations, APIs, and AI services meet security, privacy, and regulatory requirements (e.g., SOX‑sensitive data flows, access control, encryption, audit trails).
- Lead technical discovery and solution design for new API, integration, and AI‑automation initiatives