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.
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.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.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.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.Served as primary production owner for GenAI systems, managing deployments, monitoring, issue triage, and root-cause analysis across agent layers and data pipelines.Built and maintained end-to-end data and ML pipelines processing 200M+ records, supporting recurring analytics, simulations, and model-driven workflows.Automated large-scale modeling workflows generating 250+ elasticity models and delivered interactive simulators contributing to approximately 9% profit uplift for a leading CPG client.Developed a reusable MLOps framework covering CI/CD, MLflow-based experiment tracking, model versioning, drift detection, monitoring, and rollback, reused across multiple client deployments.Built a self-serve demand forecasting platform using Databricks and distributed computing, achieving ~70% accuracy and a Top-10 ranking in the M6 competition.
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.
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.
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.
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.
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.
Built and maintained end-to-end data and ML pipelines processing datasets with 200M+ records, supporting recurring analytics, simulations, and model-driven workflows.
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.
Developed a reusable MLOps framework covering CI/CD, MLflow-based experiment tracking, model versioning, drift detection, monitoring, and rollback, reused across multiple client deployments.
Developed a self-serve demand forecasting platform using Databricks and distributed computing, achieving ˜70% accuracy and a Top-10 ranking in the M6 competition.
Conducted research on NLP methods for the Telugu language, with a focus on tokenization, POS tagging, and NER tagging.Implemented HMM, CRF, LSTM, BI-LSTM, and rule-based models, improving the performance of tagging tasks.
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
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