Venkatsai Krishna Anumolu

• LLM Era Experience (2+ years)

Key Achievements:

• Architected production-ready LLM infrastructure using GPT-5 and Claude Sonnet 4.1, serving 50K+ users with 99.5% uptime
• Implemented advanced RAG pipelines with Qwen 2.5 and vector databases, improving response accuracy by 42%
• Designed agentic AI systems using MCP (Model Context Protocol) and LangGraph for complex multi-step reasoning workflows
• Led engineering team of 8 developers, implementing LLMOps practices and reducing deployment time by 45%

Technical Responsibilities:

• Built robust, scalable LLM-driven applications integrating Llama 3.1 and Mistral Large 2 with memory management
• Developed contextual retrieval systems using MCP protocols and hybrid search solutions for domain-specific queries
• Engineering leadership: Managed sprint planning, code reviews, and technical architecture decisions
• Implemented secure AI system development with proper access controls, monitoring, and Model Context Protocol integration

• Early LLM Adoption & AI Specialization (1 year)

Key Achievements:

• Engineered voice AI solutions integrating early GPT models for 100K+ MG Motors units
• Built and deployed AI-powered systems serving 1.5M daily active users with 99.2% availability
• Reduced AI infrastructure costs by 40% through efficient model selection and prompt optimization techniques
• Generated $6.3M projected annual revenue through innovative AI product implementations

Technical Responsibilities:

• Designed and implemented Text-to-SQL capabilities using fine-tuned models for natural language database queries
• Engineering management: Led team of 6 AI engineers, coordinating model deployments and system architecture
• Applied LLMOps practices for model deployment, monitoring, and maintenance at enterprise scale with proper version control

• Pre-LLM AI/ML Foundation (3 years)

Key Achievements:

• Engineered enterprise AI bots (HDFC Bank, Sephora, Asian Paints) with traditional ML and early transformer models
• Built scalable conversational AI infrastructure with custom NLU engines serving millions of interactions daily
• Engineering leadership: Managed team of 5 ML engineers, overseeing model training, deployment, and performance optimization

Technical Responsibilities:

• Developed machine learning models using traditional ML libraries (scikit-learn, PyTorch, TensorFlow)
• Managed cloud infrastructure deployments across AWS, GCP, and Azure
• Built data preprocessing pipelines using Spark, Hadoop for large-scale data processing
• Created analytics dashboards and implemented model monitoring systems

• AI/ML Foundation & Leadership (3 years) - Article Link

Key Achievements:

• Engineering leadership: Built and managed team of 13 developers across mobile, web, and AI development
• Architected scalable AI-powered ed-tech platform serving 110+ institutes with custom ML recommendation systems
• Secured $500K Series A investment through technical product demonstrations and engineering excellence

Technical Responsibilities:

• Implemented containerization and microservices architecture for AI applications
• Designed serverless AI workloads using AWS Lambda, Auto Scaling, and cloud-native services
• Built comprehensive data storage solutions with DynamoDB, Aurora, and S3
• Established security frameworks and IAM policies for AI system access control