data engineering , Software Engineering, Machine Learning, System Design, Distributed Systems, Apis, data pipelines, LLMs, Security, orchestrating monitoring agentic multi-agentic AI workflows, building and deploying AI ML systems, applied AI, front-end interfaces, Governance, scalable architectures, analytics workflows, compliance considerations for enterprise AI, generative AI, designing and deploying production-grade systems in cloud environments

Deep Learning, Deployment serving at scale, Model registries, Alerting, Ranking recommendation systems, User modelling, Applied NLP, Feature engineering, Model lifecycle management, MLOps practices, Representation learning, ML techniques, supervised learning, Scalable data pipelines, Experiment tracking, experimentation, Sequence models, Monitoring, Continuous Improvement

vectorization , Tensorflow, Pytorch, Python, CrewAI, data preprocessing, Generative AI, Hugging Face Transformers, AI agent development, vector databases, Pinecone, LangGraph, AutoGen, FAISS, Evals, Weaviate

C, Angular, React, Javascript, Ruby, Big Data, Python, AWS, Java, Vue, Gcp, Azure, AWS Bedrock, fine tuning, Privacy, Hugging Face, Go, vector databases, LangGraph, Evaluation, responsible AI, Security, guardrail techniques, LLM frameworks, LangChain, LLM deployment, embedding models, prompt engineering, SageMaker, RAG techniques, OpenAI

Knowledge of MLOps AIOps model monitoring and AI observability frameworks, Experience supporting Generative AI Machine Learning or AI platform initiatives, Analytics Dashboarding BI, Creating executive dashboards for GenAI insights usage performance cost reliability, Relevant cloud certifications Azure Data Engineer Databricks Snowflake, Data Engineering Pipeline Development, Building scalable batch and streaming pipelines for GenAI data telemetry usage model outputs

react.js , Graphql, Redux, Material UI, D3.Js, Generative AI concepts, Chart.js, Playwright, Context API, AI-assisted development tools