What a Senior } should know

Designs async architectures for LLM systems: concurrent multi-model inference, async streaming response aggregation, non-blocking RAG pipeline orchestration. Mentors team on async patterns for LLM latency optimization and throughput scaling.

Applies OOP/SOLID in LLM application architecture: strategy pattern for swappable model providers, chain of responsibility for prompt processing pipelines, abstract interfaces for embedding and retrieval components. Designs extensible LLM frameworks supporting multi-model orchestration.

Designs Elasticsearch/OpenSearch solutions for production systems. Optimizes performance and scalability. Chooses between alternative approaches. Mentors the team.

Designs Python Web Framework solutions for production systems. Optimizes performance and scalability. Chooses between alternative approaches. Mentors the team.

Designs Redis solutions for production systems. Optimizes performance and scalability. Chooses between alternative approaches. Mentors the team.

Designs S3/Object Storage solutions for production systems. Optimizes performance and scalability. Chooses between alternative approaches. Mentors the team.

Designs Task Queue solutions for production systems. Optimizes performance and scalability. Chooses between alternative approaches. Mentors the team.

Designs PostgreSQL architecture for LLM application infrastructure: optimizes pgvector for production RAG at scale, implements hybrid retrieval strategies combining semantic and keyword search, configures database architecture for conversation state management. Mentors team on PostgreSQL patterns for production LLM applications.

Designs gRPC API architecture for production LLM serving platforms — token streaming with backpressure management, efficient proto schemas for multi-modal inference with embeddings, and versioned model serving contracts supporting A/B testing. Defines error handling for inference timeout and rate limiting scenarios and optimizes serialization for high-throughput token generation.

Designs API architecture for LLM services: streaming chat completion APIs, prompt management and versioning endpoints, usage metering and billing APIs. Defines API contracts for LLM platform components. Mentors team on designing scalable LLM inference APIs.

Designs API architecture with Server-Sent Events and Streaming for production systems. Defines versioning, error handling, and pagination standards. Optimizes performance.

Designs AWS infrastructure solutions for production LLM serving — multi-GPU EC2 clusters with model parallelism, Bedrock integration for managed inference, and SageMaker endpoints for custom models. Optimizes cost through GPU instance reservation strategies, inference batching, and model quantization for reduced compute requirements. Implements security hardening for LLM workloads with model access governance, prompt logging, and data isolation.

Designs Kubernetes infrastructure for production LLM serving at scale with multi-GPU scheduling and model parallelism. Implements cost-optimized GPU cluster management with mixed node pools and intelligent workload placement. Applies security hardening for model serving endpoints and implements efficient model artifact distribution across clusters.

Designs testing strategy for LLM systems: test pyramid for prompt/chain/agent components, evaluation-driven testing with LLM-as-judge, contract tests for model provider interfaces. Optimizes test execution with mock LLM responses and selective integration testing. Mentors team on testing non-deterministic AI outputs.

Designs LLM data architecture with Pandas/Polars: training data pipeline infrastructure, evaluation benchmark management, and dataset versioning for model reproducibility. Implements data quality frameworks for detecting contamination, bias, and distribution shift. Creates organization-wide data preparation standards for LLM fine-tuning. Mentors team on scalable text processing.

Designs production-grade agent architectures using framework-agnostic patterns with pluggable LLM backends, tool registries, and observability integration. Implements advanced multi-agent systems with supervisor agents, specialized worker agents, and shared memory stores using LangGraph or custom state machines. Optimizes agent systems for production scale through intelligent caching, streaming execution, parallel tool calls, and cost-aware model routing strategies.

Architects experiment tracking systems for LLM platforms: designs custom logging for multi-stage pipelines (retrieval, generation, ranking), optimizes storage for large prompt/response artifacts, and mentors teams on tracking evaluation drift across model versions

Designs scalable LLM serving infrastructure with model routing, adaptive batching, and multi-region deployment. Establishes organization-wide prompt engineering practices with governance and audit trails. Optimizes token budgets across services through semantic caching, prompt compression, and model distillation strategies.

Designs comprehensive evaluation frameworks: automated eval with LLM-as-judge, contamination detection, statistical significance testing. Develops domain-specific benchmarks for production tasks.

Designs production fine-tuning pipelines: data curation, multi-stage training (SFT → DPO), distributed fine-tuning. Optimizes LoRA rank, learning rate, and batch size for maximum quality.

Architects MLflow infrastructure for enterprise LLM development: tracking RLHF reward model iterations, managing adapter registries, and automating evaluation suites (MT-Bench, HumanEval). Builds custom MLflow flavors for serving quantized LLMs (GPTQ, AWQ) with latency and throughput tracking.

Designs comprehensive monitoring platforms for LLM systems in production. Implements automated evaluation pipelines with LLM-as-judge, human feedback loops, and A/B test instrumentation. Builds cost optimization dashboards tracking token usage across model versions and prompt variants. Mentors team on LLMOps observability patterns.

Designs LLM serving architecture: multi-model gateway with intelligent routing, speculative decoding for latency optimization, and disaggregated serving (prefill/decode separation). Implements cost optimization: token budget management, caching layers for repeated prompts, and model cascade strategies. Creates LLM serving benchmarks and capacity planning models. Mentors team on production LLM infrastructure.

Architects LLM training infrastructure in PyTorch. Implements custom attention, positional encodings and model parallelism. Optimizes memory with gradient checkpointing, mixed precision and Flash Attention. Designs evaluation frameworks for LM quality. Mentors team on transformer internals.

Designs scalable RAG platforms with graph-augmented retrieval, contextual compression, and adaptive chunking strategies. Implements knowledge graph integration for structured reasoning and mentors team on RAG evaluation methodology and hallucination mitigation.

Architects production LLM systems with optimized serving (vLLM, TGI), model parallelism, and efficient batching strategies. Designs evaluation frameworks for model quality, safety, and bias detection. Implements advanced techniques: RLHF, constitutional AI, and chain-of-thought optimization.

Designs scalable vector DB infrastructure: sharding, replication, hybrid search. Optimizes index parameters for trade-offs between recall, latency, and memory with millions of vectors.

Designs production AI agent architectures with sophisticated reasoning capabilities — tree-of-thought planning, self-reflection and correction, and dynamic strategy adaptation based on task complexity analysis. Implements multi-agent systems with agent specialization, communication protocols, and consensus mechanisms for complex problem decomposition. Architects agent infrastructure for scale including distributed tool execution, persistent agent memory with vector stores, and real-time observability of agent reasoning chains and cost metrics.

Designs complex multi-model pipelines using ChatGPT and Claude. Implements fallback strategies, A/B testing of models, and optimizes costs through request routing between providers.

Designs scalable MCP infrastructure with server composition, tool orchestration, and fallback strategies. Implements protocol extensions for custom capabilities and mentors team on context window optimization with MCP tools.

Designs advanced prompt engineering systems: automated prompt tuning (DSPy, OPRO), prompt chaining, meta-prompting. Optimizes prompts for minimal latency and cost while maintaining quality.