Optimize LLM behavior using structured prompting, role assignment, and controlled output formatting.

Design scalable middleware to manage API requests, rate limits, caching, and token budgets for efficient LLM apps.

Create intuitive, user-centered interfaces that integrate feedback loops to continuously improve model responses and user trust.

Analyze AI workloads to define logical microservice boundaries and implement modular LangChain components communicating via gRPC.

Apply containerization and orchestration using Docker, ECR, K8s to deploy, scale, and monitor LangChain services with health checks and telemetry.

Evaluate and strengthen resilience by implementing OpenTelemetry tracing, Prometheus metrics, and chaos testing to measure and improve recovery.

Design automated CI/CD pipelines for LLM deployments using containerization and infrastructure as code.

Apply security best practices including API protection, prompt injection prevention, and compliance frameworks.

Configure production monitoring, auto-scaling, and cost optimization for enterprise LLM systems.

Apply decoding strategies (e.g., temperature, top-k, top-p, beam search) to control model outputs for quality, diversity, and relevance.

Evaluate AI-generated text using automated metrics and frameworks to systematically assess fluency, coherence, and factual accuracy.

Implement parameter-efficient fine-tuning (PEFT) techniques to create domain-adapted foundation models while balancing cost-performance trade-offs.

Apply sentence-transformers to embed documents and validate recall using FAISS vector indices and systematic retrieval tests.

Diagnose embedding issues by visualizing with UMAP, spotting anomalies, and cleaning data via cluster analysis workflows.

Evaluate embedding models on cost, latency, and accuracy to recommend the best candidates for production deployment.

Analyze LLM architectures and foundation models for specific use cases.

Implement fine-tuning techniques using industry-standard tools and frameworks.

Deploy LLM models in production environments with security and optimization.

Design scalable LLM API architectures using microservices patterns, load balancing, and caching for high-throughput applications.

Implement enterprise security including authentication, authorization, rate limiting, and prompt injection protection.

Deploy monitoring systems and optimize performance achieving 99.9% uptime and sub-100ms response times.

Evaluate AI use cases by applying key Responsible AI principles such as fairness, transparency, and accountability.

Identify and document potential risks and biases across data, models, and user interactions using structured ethical design tools.

Develop and communicate stakeholder-ready presentations and documentation that clearly articulate Responsible AI design decisions.

Map model metrics to business metrics, and define baselines, counterfactuals, and a measurement plan.

Design experiments, compute lift and confidence intervals, and plan guardrails.

Quantify ROI and risk, build an impact dashboard, and craft an executive story with clear next steps.