iOS Engineer (AI Integration)
LocationBengaluru, Karnataka, India
Typefull-time
SenioritySenior
Experience6+ yrs
Company size1,001–5,000 people
PostedSep 29, 2026 · 2w ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
6+ years of professional iOS development experience with Swift
Hands on experience integrating LLM APIs in production environments
Working knowledge of MCP (Model Context Protocol) frameworks, agentic workflows, and tool use patterns
Proven track record of debugging memory leaks with Instruments, analyzing crash trends, and optimizing startup performance
Strong grasp of modular architecture, dependency management (Swift Package Manager), and testability
Proven specialization in at least 2 to 3 of Swift Concurrency, SwiftUI, Combine, Core Data, SwiftData, Networking with URLSession, gRPC, Protocol Buffers, BackgroundTasks framework
Experience building iOS apps in banking, fintech, or other regulated environments
Familiarity with crash and observability tools such as Firebase Crashlytics, Sentry, or Datadog
Experience with accessibility standards (VoiceOver, Dynamic Type)
Exposure to on device ML (Core ML, Apple Foundation Models)
Skills
Swift
SwiftUI
Swift Package Manager
LLM APIs
MCP (Model Context Protocol)
Xcode Instruments
Swift Concurrency (async/await, Actors, Task Groups)
Combine
Core Data
SwiftData
Networking with URLSession
gRPC
Protocol Buffers
BackgroundTasks framework
Firebase Crashlytics
Sentry
Datadog
Core ML
Apple Foundation Models
About the Role
We are looking for an experienced iOS Engineer to build and maintain large scale, modular iOS applications using modern Apple technologies, with a strong focus on AI integration. You will design scalable architectures, optimize performance, and deliver high quality experiences across iPhone and iPad, working alongside multiple feature teams.
Key Responsibilities
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Design and build scalable, modular iOS application architectures that support multiple feature teams working in parallel.
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Develop advanced SwiftUI interfaces with a strong focus on responsiveness and accessibility.
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Integrate LLM APIs into production iOS applications, including token management, context handling, and resilient error handling.
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Implement agentic workflows within the app, including multi agent orchestration and tool use patterns, leveraging MCP (Model Context Protocol) frameworks.
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Evaluate LLM trade offs (latency, cost, accuracy, on device vs cloud) and recommend the right approach for each use case.
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Implement crash monitoring and observability solutions, and analyze crash trends to drive stability improvements.
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Diagnose and resolve memory, performance, and concurrency issues using Xcode Instruments and profiling tools.
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Optimize app startup time and overall runtime performance.
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Apply modern dependency management and architectural patterns to deliver maintainable, testable code.
Required Qualifications
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6+ years of professional iOS development experience with Swift, delivering large scale applications in production.
•
Hands on experience integrating LLM APIs in production environments.
•
Working knowledge of MCP (Model Context Protocol) frameworks, agentic workflows, and tool use patterns.
•
Proven track record of debugging memory leaks with Instruments, analyzing crash trends, and optimizing startup performance.
•
Strong grasp of modular architecture, dependency management (e.g. Swift Package Manager), and testability.
•
Proven specialization in at least 2 to 3 of the following:
•
Swift Concurrency (async/await, Actors, Task Groups)
•
SwiftUI and Combine
•
Core Data or SwiftData
•
Networking with URLSession, gRPC, or Protocol Buffers
•
Background processing with the BackgroundTasks framework
Preferred Qualifications
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Experience building iOS apps in banking, fintech, or other regulated environments.
•
Familiarity with crash and observability tools such as Firebase Crashlytics, Sentry, or Datadog.
•
Experience with accessibility standards (VoiceOver, Dynamic Type).
•
Exposure to on device ML (Core ML, Apple Foundation Models).
What Success Looks Like
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Stable, fast, accessible apps with measurable improvements in crash free rate and startup time.
•
AI features shipped to production that are reliable, cost efficient, and fail gracefully.
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Clean architecture that lets multiple feature teams ship independently.