Senior AI Engineer — Inference & Agent Systems

About this role

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Skills you'll use

What you'll do

• BLR/Remote India
• Drive TTFT below 400ms for multi-step agent pipelines
• Streaming optimization: first token to user while sub-agents are still running
• KV cache strategy, prompt compression, dynamic context window management
• Multi-provider routing: model selection by latency, cost, and task type across OpenAI, Anthropic, Gemini, and open-weight models
• Design and implement Plan-Execute-Synthesize pipelines that run sub-agents in parallel DAGs, not sequential chains
• Build reliable orchestration on top of Temporal: retries, timeouts, partial failure recovery, idempotency
• Structured output enforcement: JSON schema validation, retry loops on malformed LLM output, graceful degradation

Who can apply

• Tool call design: schema design that LLMs actually follow reliably across providers
• Own the eval framework end to end: ground truth datasets, automated scoring pipelines, regression detection on every PR
• LLM-as-judge pipelines for qualitative output assessment
• Latency regression testing - p50/p95/p99 tracked across every deployment
• Adversarial test case design: ambiguous queries, missing data, conflicting sources, malformed tool responses
• Model serving and cold start optimization
• Async worker architecture for parallel sub-agent execution
• Observability: trace every token, every tool call, every synthesis step

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