Data Science / ML Engineer
Bangalore, Karnataka, IndiaJobNo compensation foundPosted 5mo agoVerified open 3 days ago
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At a glance
Compensation
No compensation found
Location
Bangalore, Karnataka, India
Work Authorization
Not specified
Job overview
Oolka is seeking a Data Science / ML Engineer to design and implement asynchronous multi‑agent orchestration, own end‑to‑end latency, and build resilient inference pipelines for AI workloads, while optimizing caching and real‑time communication infrastructure.
Skills & qualifications
RequiredNice to have
Skills
RedisRESTMachine LearningTensorFlowCommunication ProtocolsDatabase CachingArtificial IntelligenceContent Distribution NetworksAsync/Event‑Driven ArchitecturesML/AI Inference ScalingCaching StrategiesMessage QueuesReal‑Time Communication ProtocolsTensorFlow ServingTritonLLM IntegrationModel Quantization
Qualifications
3+ 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.
- Optimize credit data retrieval and caching strategies.
Requirements:
- 3+ 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 optimization (batching, caching, model quantization).
- Knowledge of conversation state management and context handling.
- Has debugged production issues under high AI inference load.
Growth Path:
- Direct impact on customer subscription retention through performance.
- Exposure to cutting-edge AI infrastructure challenges.
- Ownership of technical decisions affecting revenue-generating conversations.
- Path to leading an AI platform team as you scale.
You've read the whole posting — now see how you match it.