The AES Corporation

* Led organization-wide MLOps standardization by successfully deploying multiple AI applications across Supply Chain, and Clean Energy initiatives, establishing organizational best practices for ML model deployment and maintenance.

* Accelerated ML model deployment time from 7 days to 1 day by implementing standardized templates and processes.

* Reduced artifact registry costs by 40% by implementing and standardizing a tagging process of code and docker images.

* Established a comprehensive MLOps tools ecosystem supporting model deployment, monitoring, maintenance, and security.

Machine Learning Engineer (Digital)

Dell Technologies

* Enhanced customer intent and sales opportunity identification as a result of increasing LLM’s accuracy by 30%. Achieved this by enabling a Retrieval Augment Generation (RAG) architecture with PGVector containing earning transcripts and news embeddings.

* Streamlined model management processes, achieving a 50% reduction in time-to-market for new LLM applications. Accomplished this by developing LLMOps functionalities with MLflow, improving evaluation accuracy by 25% and operational efficiency.

* Improved the consumption of customer growth data (1M+ records) from 2 min. to 2 sec. with online feature serving. Achieved this by implementing a Feature Store with Feast to consolidate multiple features into a single dataset for machine learning workloads.

* Increased content generation efficiency by 50%+ and a potential cost reduction of 95% on social media platforms by fine-tuning Meta’s Llama v2 models with Dell’s marketing data. Utilized Python, Hugging Face, QLora, PEFT, and Llama.Cpp.

* Developed a Translation Engine utilizing Spark for distributed processing, achieving an 85% reduction in translation process time by enabling high-volume, parallel data translations. This accelerated ingestion processes and served as a reference for big data transformation workloads.

* Facilitated 5 trainings on Artificial Intelligence (AI), Machine Learning and Generative AI, impacting 500 team members and increasing their technical proficiency while supporting their career progression.

Machine Learning Engineer (Growth)

* Collaborated in a small team (4 engineers) that developed a cutting-edge campaign automation solution that reduced execution times by over 50% using Apache Airflow and Docker, which resulted in fresher leads for Sales and optimized conversion rates.

* Contributed to engineering of SQL triggers for the pioneering US Small Business Reactivation campaign, resulting in a 26% increase in revenue per buyer and a 13% uplift in reactivation rates within just one month.

* Led the development of a global campaign lead pipeline that catalyzed a $50M increase in incremental revenue within a quarter, by implementing a creative lead selection strategy with Python, SQL, and Airflow.

* Contributed to a Python and SQL-based quality check solution, enhancing data integrity and coverage by 40%. This solution was adopted by 30+ campaigns, which increased the quality and trust of 10M+ leads and contacts.

* Facilitated over 10 technical trainings on Git, GitLab, SQL, Python, and Airflow, impacting 20 team members and increasing their technical proficiency.

Data Engineer (Campaigns)

Grainger

* Enhanced operational oversight by developing a Power Bi dashboard that tracked process performance, team metrics, and quality metrics, facilitating the successful introduction of over 60,000 new products in Canada and Mexico.

* Reduced review processing time in 98% (from 3 hrs. to 2 min.) by automating the modification and addition of macros in 100+ Excel files using a Python script. This saved around 30 hours per review cycle, enhancing productivity and reducing errors.

* Delivered strategic product insights by analyzing sales metrics (Items Sold, Revenue, Profit, Profit Margin) for newly introduced products using SQL. This analysis yielded vital trends at 1, 3, and 6 months post-launch, refining sales and product strategies.

* Implemented a Power BI Dataflow with a Star Schema, optimizing product information retrieval, which boosted report generation efficiency by 20% and enhanced KPI analysis driving operational insights and decision-making effectiveness.

* Implemented a Power BI Dataflow with the JIRA API, resulting in the reduction of 50 hrs/year of manual labor. This was achieved by scheduling the retrieval of board tickets information and metrics.

Senior Data Analyst (Operations)

Dell

* Improved customer engagement and satisfaction by strategically coordinating the utilization of $10,000+ in training credits. Achieved this by identifying unused credits among customers in Brazil and organizing targeted training sessions in collaboration with Brazil's Account Managers and Sales Representatives.

* Partnered with the management team to standardize the complexity report for customer incidents, streamlining incident analysis. This initiative improved productivity estimations by 25%, ensuring enhanced service response based on incident complexity levels.

Data Analyst (Services)

Machine Learning Engineer | Proficient in Python & SQL | Championing AI-Driven Efficiency & Automation

MLOps & Machine Learning Engineer with 5 years of experience building production-grade AI systems. Specialized in Large Language Model applications, having improved LLM accuracy by 30% using RAG architectures and reduced inference costs by 95% through model optimization (LLaMA, LoRA, PEFT).

Led end-to-end MLOps initiatives that cut model deployment time from 7 days to 24 hours and reduced infrastructure costs by 40%. Expertise spans the full ML stack: feature stores (Feast), vector databases (PGVector), model registry (MLflow), and orchestration (Airflow, Kubernetes).

Currently, driving AI transformation at AES, previously at Dell Technologies, focusing on scalable ML systems that deliver measurable business impact. Microsoft Certified Trainer and regular speaker at AI conferences, committed to knowledge sharing and mentoring in the ML community.

Technical stack: PyTorch, TensorFlow, HuggingFace, JAX, Kubernetes, Azure AI, GCP Vertex AI

Open to relocation opportunities!

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