Senior AI/ML Engineer with around 4 years of experience building, deploying, and operating production-grade machine learning and Generative AI systems. Strong foundation in software engineering, cloud-native architecture…
Senior AI/ML Engineer with around 4 years of experience building, deploying, and operating production-grade machine learning and Generative AI systems. Strong foundation in software engineering, cloud-native architectures, ML systems design, and MLOps, with hands-on ownership across the full lifecycle—from problem formulation and system design to deployment, monitoring, and iteration in production. Experienced in building decision-support platforms using classical ML, LLMs, and RAG pipelines over large enterprise datasets, and translating ambiguous business requirements into reliable, scalable AI systems used by real stakeholders.
<p>Owned end-to-end design, development, and deployment of a production AI tool enabling descriptive analysis, simulations, and insight generation over enterprise datasets, actively used by 5–10 daily users across pricing, planning, and marketing teams.</p><p></p><p>Designed a modular, multi-agent architecture with a centralized orchestrator routing queries across Text-to-SQL, EDA, statistical analysis, hypothesis testing, code execution, and simulation workflows, improving extensibility and debuggability.</p><p></p><p>Built LLM-powered analytical copilots using OpenAI (GPT-3.5/4.1), LangChain, and tool calling, with deterministic prompts, schema grounding, and guardrails to ensure reliable and auditable outputs.</p><p></p><p>Implemented RAG pipelines leveraging document chunking, metadata filtering, embeddings, and vector databases to ground LLM responses on structured data and business documentation for internal and client-facing use cases.</p><p></p><p>Served as primary production owner for GenAI systems, managing deployments, monitoring, issue triage, and root-cause analysis across agent layers and data pipelines.</p><p></p><p>Built and maintained end-to-end data and ML pipelines processing 200M+ records, supporting recurring analytics, simulations, and model-driven workflows.</p><p></p><p>Automated large-scale modeling workflows generating 250+ elasticity models and delivered interactive simulators contributing to approximately 9% profit uplift for a leading CPG client.</p><p></p><p>Developed a reusable MLOps framework covering CI/CD, MLflow-based experiment tracking, model versioning, drift detection, monitoring, and rollback, reused across multiple client deployments.</p><p></p><p>Built a self-serve demand forecasting platform using Databricks and distributed computing, achieving ~70% accuracy and a Top-10 ranking in the M6 competition.</p>
<p>Owned design, development, and deployment of a production AI tool enabling descriptive analysis, simulations, and insight generation over enterprise datasets; actively used by 5–10 users daily across pricing, planning and marketing teams.<br>
Designed a modular, multi-agent architecture with a centralized orchestrator to route user queries across Text-to-SQL, EDA, statistical analysis, hypothesis testing, code execution, and simulation workflows, improving extensibility and debuggability.<br>
Built LLM-powered analytical copilots using OpenAI (GPT-3.5/4.1), LangChain, and tool calling, with deterministic prompts, schema grounding, and guardrails to ensure reliable and auditable outputs.<br>
Implemented RAG pipelines using document chunking, metadata filtering, embeddings, and vector databases to ground LLM responses on structured data and business documentation; supported both internal and client-facing use cases.<br>
Acted as the primary production owner for GenAI systems, handling deployments, monitoring, issue triage, and root-cause analysis for agent layers and associated data pipelines.<br>
Built and maintained end-to-end data and ML pipelines processing datasets with 200M+ records, supporting recurring analytics, simulations, and model-driven workflows.<br>
Automated large-scale modeling workflows generating 250+ elasticity models and delivered interactive simulators that contributed to approximately 9% profit uplift for a leading CPG client.<br>
Developed a reusable MLOps framework covering CI/CD, MLflow-based experiment tracking, model versioning, drift detection, monitoring, and rollback, reused across multiple client deployments.<br>
Developed a self-serve demand forecasting platform using Databricks and distributed computing, achieving ˜70% accuracy and a Top-10 ranking in the M6 competition.</p>
<p>Conducted research on NLP methods for the Telugu language, with a focus on tokenization, POS tagging, and NER tagging.</p><p></p><p>Implemented HMM, CRF, LSTM, BI-LSTM, and rule-based models, improving the performance of tagging tasks.</p>
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
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