Bhargav Raju Dalali
Ai Ml Engineer @Accenture
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WORK HISTORY
Ai Ml Engineer @Accenture
US
Architected and deployed production-grade machine learning models on the Databricks Lakehouse using Python, PySpark, Delta Lake, Spark MLlib, and MLflow, cutting model training runtime by 30%.• Developed GenAI solutions by integrating LLMs with RAG architectures using LangChain and Pinecone vector databases, enabling enterprise-scale knowledge search and reducing manual information retrieval time across teams.• Engineered end-to-end MLOps pipelines with GitHub Actions, Docker, Kubernetes, and MLflow, automating CI/CD for ML models and accelerating deployment cycles from 2 weeks to 5 days with improved release reliability.• Optimized distributed GPU training workflows leveraging Spark, MosaicML, and cloud-based compute, achieving 25% faster model convergence while increasing GPU utilization efficiency and lowering training costs.• Implemented model monitoring, governance, and lifecycle management using Databricks Model Registry, MLflow, and Prometheus, enabling drift detection, version control, auditability, and alignment with Responsible / Ethical AI standards.• Collaborated cross-functionally with data engineering, product, and business stakeholders to translate requirements into scalable AI/ML solutions, driving 38% platform adoption growth and reducing insight-to-action time by 32%.
EDUCATION
Rowan University
Master of Science - MS, Cybersecurity
ABOUT BHARGAV RAJU DALALI
Senior AI/ML Engineer with 5+ years of experience building and deploying production-grade machine learning and Generative AI systems in enterprise environments. I specialize in taking AI models from experimentation to scalable, compliant, and high-impact production systems using cloud-native and MLOps-driven architectures.At Accenture and Cognizant, I’ve led the design and deployment of ML and GenAI solutions across Databricks, AWS, and Kubernetes—delivering measurable business impact, including 30%+ performance improvements, faster deployment cycles, and increased platform adoption. My work spans traditional ML, deep learning, and modern GenAI use cases such as Retrieval-Augmented Generation (RAG), enterprise knowledge search, and conversational AI.I’m particularly strong in MLOps and model lifecycle management—building CI/CD pipelines, model monitoring, drift detection, and governance frameworks using MLflow, Prometheus, and GitHub Actions to ensure reliability, auditability, and Responsible AI compliance at scale.I enjoy collaborating closely with data engineering, product, and business teams to translate complex requirements into practical AI solutions that drive efficiency, adoption, and ROI. Currently focused on large-scale GenAI systems, distributed training, and production ML platforms.
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