Lead AI Engineer
Bangalore, Karnataka, IndiaJobNo compensation foundPosted 4mo agoVerified open 2 days ago
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At a glance
Compensation
No compensation found
Location
Bangalore, Karnataka, India
Work Authorization
Not specified
Job overview
The Lead AI Engineer will design and implement asynchronous multi-agent orchestration, own latency from user message to AI response, and build resilient inference pipelines that degrade gracefully under load while optimizing caching and real-time communication.
Skills & qualifications
RequiredNice to have
Skills
Artificial IntelligenceRESTCommunication ProtocolsContent Distribution NetworksTensorFlowDatabase CachingMachine LearningRedisAsync Event-Driven ArchitecturesProduction SystemsScaling ML AI InferenceCaching StrategiesContent Delivery NetworkMessage QueuesReal-Time Communication ProtocolsWebSocket StreamingTensorFlow ServingTritonModel QuantisationBatchingConversation State ManagementAI Model Serving Frameworks
Qualifications
6+ Years Production Systems Experience
Full job description
Responsibilities:
- Design and implement asynchronous multi-agent orchestration.
- Own end-to-end latency from user message to AI response.
- Build resilient inference pipelines that gracefully degrade under load.
- Implement intelligent request routing and load balancing for AI workloads.
- Migrate critical AI conversation flow from monolith to dedicated services.
- Implement WebSocket/streaming infrastructure for real-time chat.
- Design circuit breakers and fallback strategies for AI model failures.
- Build comprehensive observability for AI system performance.
- Optimise credit data retrieval and caching strategies.
Requirements:
- 6+ years building production systems handling > 10k concurrent users.
- Proven experience with async/event-driven architectures (not just REST APIs).
- Hands-on experience scaling ML/AI inference in production.
- Deep understanding of caching strategies (Redis, in-memory, CDN).
- Experience with message queues and real-time communication protocols.
AI-Specific Expertise:
- Built systems integrating multiple LLM/AI models in production.
- Experience with AI model serving frameworks (TensorFlow Serving, Triton, etc. )
- Understanding of AI inference optimisation (batching, caching, model quantisation).
- Knowledge of conversation state management and context handling.
- Has debugged production issues under high AI inference load.
You've read the whole posting — now see how you match it.