Rakesh Sheshadri — Associate Partner

As a Practice Manager, I have led AI/ML initiatives by developing capabilities, mentoring teams, and supporting organization-wide growth. I have strong experience managing RFP/RFQ and proactive pursuits end-to-end, including requirement analysis, solution design, technical proposals, PoCs, and executive presentations that enabled multi-million-dollar wins. I excel at translating AI capabilities into clear business value, bridging research, engineering, and strategy. I have broad hands-on ML experience, including supervised learning (Linear/Logistic Regression, Random Forest, XGBoost, SVM) and unsupervised learning (K-Means, DBSCAN, PCA, Hierarchical Clustering). I also have experience in statistical modeling, including probability theory, hypothesis testing, ANOVA, time-series analysis (ARIMA), regression modeling, and feature engineering. I have strong NLP experience in preprocessing, tokenization, embeddings, NER, sentiment analysis, text classification, topic modeling, and LLM-based conversational systems using modern embeddings and vector search. My deep learning expertise covers neural networks, CNNs, RNNs/LSTMs, Transformers, attention, and transfer learning, using TensorFlow, Keras, and PyTorch. I am proficient in Python (OOP, async, multiprocessing), Java (collections, multithreading, Spring), C++ (memory mgmt, STL, templates), and Go (goroutines, channels, interfaces). I have experience with RAG including contextual chunking, vector/hybrid search, reranking, multimodal RAG, and advanced approaches like self-refining, agentic, and graph-based RAG. In Agentic AI, I work with patterns such as ReAct, MRKL, auto-planning, self-reflection, tool orchestration, and memory, using CrewAI, Google ADK, and LangGraph/LangChain. My AI engineering skills include prompt/context engineering, retrieval schema design, evaluation, guardrails, grounding, and observability—focused on scalable, cost-efficient, safe LLM systems. Cloud experience: GCP: Vertex AI, BigQuery, Cloud Storage, Pub/Sub, Cloud Run, GKE, Discovery Engine, Secret Manager etc. AWS: S3, Lambda, EC2, RDS, DynamoDB, SageMaker, SQS, API Gateway, CloudWatch etc. Azure: Azure AI, Azure OpenAI, Cognitive Search, App Services, AKS, Key Vault etc. I also have experience in Conversational AI with Google CCAI, Dialogflow CX, and Vertex AI.

Stackforce AI infers this person is a multi-cloud AI architect with expertise in AI/ML solutions for enterprise applications.

Location: Mysuru, Karnataka, India

Experience: 12 yrs 11 mos