Machine Learning Ml Engineer

Artificial Intelligence and Machine Learning

AI / ML EngInEEr

Name: Harsha Vardhan

E-Mail: *************************@*****.***

Phone: 689-***-****

Professional Summary:

AI/ML Engineer with 6+ years of experience building and deploying machine learning models, implementing AI-powered applications, and optimizing complicated algorithms. Proven record of increasing performance, lowering expenses, and providing data-driven insights. Strong knowledge of Python, TensorFlow, PyTorch, and cloud-based AI/ML services. Enthusiastic about applying innovative technology to real-world situations. Data-driven strategies promote cooperation for creative AI and machine learning products. Demonstrates proficiency in NLP approaches and adaptability to address language processing challenges. Proficient in Shell scripting to optimize system and Linux operations. Experienced with Large Language Models (LLMs) and large-scale language data management. Used Python extensively for AI and NLP, resulting in easy development and deployment. Proficient in rapid engineering and GenAI deployments. Demonstrates strong knowledge of AI and ML principles, enabling complicated issue resolution. Knowledgeable in the use of data modeling tools including Oracle SQL Developer Data Modeler, IBM Infosphere Data Architect, and ER/Studio.

Proficiency with big data processing and analytics using Databricks. Created and implemented machine learning models with Databricks to forecast consumer behavior and enhance corporate strategies.

Skilled Data Integration Professional with more experience in Oracle-based ETL process design, development, and management to support business intelligence and data warehousing programs. Efficient at utilizing FTP/SFTP tools and technologies to guarantee safe file administration and dependable data communication.

In-depth knowledge and experience in designing, developing, and deploying Big Data projects using Hadoop, Data Analytics, SQL, and Distributed Machine Learning frameworks. Used Snowflake for analytics, ETL, and large-scale data warehousing. Good knowledge and experience in Impala, HBase and MapReduce. Proficient in automating build processes, application deployments, and continuous integration systems such as Jenkins and TFS.

Technical Skills:

Programming and

Scripting

UNIX Shell Scripting, JAVA, Python, C, C++, SQL

Machine Learning

Platforms

Azure OpenAI and Azure ML Studio, TensorFlow and PyTorch Database

Oracle, MySQL, SQL Server, HBASE

NLP Techniques

SpaCy, NLTK, and Hugging Face Transformers

Auto ML Tools

Google AutoML and Azure AutoML

Data Concepts

ETL tools – Informatica

Cloud Platforms

AWS, Azure, GCP

Prompt Generation

Prompt Flow

Big Data Technologi e

Data Bricks, Hadoop, Oozie, HBASE, HDFS, Hive, PIG, SPARK, DELTA LAKE, MLFLOW, Snowflake

Others

MS Project, MS Office

PROFESSIONAL EXPERIENCE :

T-Mobile, Bellevue, Washington Jun 2023 - Present

Role: AI/ML Engineer

Responsibilities:

• Assisted cross-functional teams in implementing and utilizing LLMs for various business applications.

• Used Azure ML to create, deploy, and optimize ML solutions for real-world situations.

• Developed, trained, and deployed machine learning models on AWS using Amazon Sage Maker.

• Utilized Python to run machine learning techniques and work with various data types, including JSON and XML.

• Used AWS Sage Maker to improve machine learning model creation and deployment, streamlining engineering workflows for productivity and scalability.

• Managed project timelines, tracked issues, and utilized JIRA.

• Used Prompt Flow's user-friendly UI and AI to create engaging tales and streamline creative processes.

• Implemented LLMs in automated customer service systems for increased responsiveness and efficiency.

• Analyzed and modeled data to optimize consumer interactions.

• Worked with product managers to develop needs and specifications for LLM-based features and functionality.

• Expert in using OpenAI's GPT models to create innovative engineering solutions and improve automation processes.

• Proficient in PowerShell scripting, creating effective automation solutions for quick engineering jobs.

• Used SpaCy to analyze text and quickly discover relevant information.

• Used Prompt Flow to create compelling material efficiently.

• Updated Python scripts to match training data with AWS Cloud Search database and label document responses for categorization.

• Expertise in constructing instruction-based LLMs and fine-tuning AI models with Lora, QLoRA, Dreambooth, Control Net, and other sophisticated techniques.

• Used Azure Devops to deploy and maintain machine learning models with high availability and scalability.

• Integrated custom knowledge bases with Vector DB and RAG systems to provide domainspecific information to models and improve content precision and relevance.

• Led and owned the ML (Machine Learning) service development lifecycle, from exploratory analysis to model deployment in production.

• Utilized various machine learning methods, such as random forest, boosted tree, SVM, SGD, neural network, and deep learning, with PyTorch and TensorFlow. Created a new data scheme for Machine Learning's consumption store.

• Proficient in Python for scripting, data manipulation, and AI model development, with seamless interface with GCP and RAG systems.

• 25% more efficient data manipulation was accomplished with Pandas and NumPy, and Matplotlib provided lucid insights.

• Created machine learning models using PyTorch and TensorFlow.

• Experienced with GenAI products including Vertex AI, Open AI API, Llama index, Langchain, Stable Diffusion, and Midjourney for generative AI and LLM integrations.

• Experienced with SQL/NoSQL databases and queries for effective data retrieval and storage in AI/ML pipelines.

• Demonstrates experience implementing and administering AI/ML models on cloud platforms such as GCP (recommended), AWS, and Azure.

• Worked with ML toolkits and Python frameworks for deep learning, including model creation, serving, and training/fine-tuning.

• Experienced with MLOps best practices for scalable and effective deployment of machine learning models utilizing Terraform, Kubernetes, and Docker.

• Strong knowledge of probability and statistics, as well as machine learning concepts. Environment: LLMs, OpenAI GPT models, PowerShell, SpaCy, Prompt Flow, Python, AWS Cloud Search, AutoML techniques, TensorFlow, PyTorch, SQL, Cloud services, Azure ML, Amazon Sage Maker, JSON, XML, JIRA.

Amex, New York Sept 2022 – May 2023 Role: AI/ML Engineer Responsibilities:

• Developed innovative solutions for individualized customer interactions and automation through LLM implementation and refinement.

• Used prompt engineering techniques to optimize LLM interactions, resulting in personalized and contextually suitable responses.

• Optimized open-source LLMs from platforms such as Hugging Face and Llama3 to meet customer needs.

• Proficient in Shell scripting to optimize engineering procedures and improve system efficiency.

• Used OpenAI's advanced GPT models to provide efficient and automated solutions across multiple disciplines, supported by extensive knowledge and experience.

• Design and implement cutting-edge generative models for NLP applications such as text generation, completion, translation, and summarization.

• Development experience with commercial and opensource chatbots and text processing tools using LLMs.

• Utilized GCP and RAG approaches to efficiently solve real-world problems with AI.

• Used GCP AutoML to create bespoke machine learning models quickly and accurately, increasing project efficiency.

• Extensive grasp of NLP approaches and huge language models, enabling efficient implementation of RAG systems for rapid engineering.

• Led the deployment, maintenance, and scalability of LLM solutions across digital platforms, ensuring stability and performance.

• Expertise in using PyTorch for effective deep learning model building and implementation.

• Used Prompt Flow's AI prompts and tools to create high-quality material for multiple projects.

• Researched and analyzed ways to improve LLM functionality and enhance consumer experiences.

• Expertise in applying RAG methodologies to enhance LLM solutions for improved contextual understanding and response production.

• Worked with SAs and cross-functional teams to understand business requirements and provide AI/ML solutions that fulfill client objectives.

• Stay up to date on generative AI and LLM breakthroughs to improve skills and expertise.

• Expertise in reviewing and preparing huge datasets from structured and unstructured sources, including documents and photos, to ensure data quality and integrity.

• Developed data pipelines to train and evaluate generative models.

• Demonstrates ability to explain the consequences of hallucinations to corporate stakeholders and apply behavioral analytic approaches.

• Developed Guardrails for LLMs utilizing open-source and cloud-native models to assure safe and trustworthy AI behavior.

• Applied various machine learning methods, such as random forest, boosted tree, SVM, SGD, neural network, and deep learning with TensorFlow.

• Guided junior engineers and team members on LLM implementation and proper engineering practices.

• Analyzed and prepared data using a historical model in AZURE ML.

• Worked with data scientists to create and improve algorithms for LLM-based applications.

• Collaborated with UX/UI designers to seamlessly integrate LLMs into digital interfaces and applications. Environment: LLMs, Hugging Face, PowerShell, OpenAI GPT models, R (data visualization), Azure ML, Power BI, Prompt Flow, PyTorch, TensorFlow, UX/UI design integration. Cognizant technology Solutions Pvt. Ltd. – Bangalore, India Jan 2020 - April 2022 Role: AWS DATA ENGINEER

Responsibilities:

• Worked with end users, business analysts, and architects to acquire comprehensive requirements for data analysis.

• Designed and developed scalable ETL pipelines using AWS Glue, orchestrating data movement and transformation from various sources into Amazon Redshift.

• Utilized Amazon S3 for data lake storage, implementing data partitioning and lifecycle policies to optimize storage costs and access efficiency.

• Created data transformation scripts in Python and PySpark, performing data cleansing, enrichment, and aggregation.

• Oversaw and enhanced Amazon Redshift clusters, encompassing workload management, query performance tuning, and schema design.

• Implemented data warehousing solutions, employing Amazon Athena and Redshift Spectrum to query S3 data.

• Implemented frameworks for big data processing, such as Amazon EMR, and used Spark and Hive for extensive data analysis.

• Developed real-time data streaming solutions using Amazon Kinesis and AWS Lambda, enabling realtime analytics and monitoring.

• Integrated AWS Lambda with other AWS services for serverless data processing and automation.

• AWS Identity and Access Management (IAM) policies were put into place to protect data and provide secure access management.

• Streamlining the setup of AWS resources through automated infrastructure deployment and configuration with AWS CloudFormation and Terraform.

• AWS CodePipeline and CodeBuild were used to implement CI/CD pipelines, guaranteeing effective data solution deployment and integration.

• Used Amazon CloudWatch to monitor and improve data operations, including dashboards and alerts for performance monitoring.

• Integrated Airflow with several AWS services, such as Glue, Redshift, S3, and RDS, to manage end-toend data processing operations.

• Autonomously transferred and transformed data between various AWS services by utilizing Airflow operators.

• Experienced in designing and deploying AWS solutions using EC2, S3, RDS, EBS, Elastic Load Balancer, and Auto-scaling groups.

• Automated regular processes, like data intake, processing, and transformation, using Lambda in response to events in S3, DynamoDB, and other AWS services.

• Analyzed business requirements, system requirements, and data mapping, and communicated these effectively to developers.

• Documented functional requirements and supplementary requirements.

• Developed SQL joins and queries for data retrieval, accessed data for analysis, and exported data into CSV and Excel files.

• Extracted large volumes of data from various sources, performed transformations, and loaded the data into target structures.

• Created data pipelines and performed data cleaning and preprocessing on CSV files to convert them into a tabular format for business intelligence.

• Utilizing Postgres, Python, AWS, Tableau, and Pivot Tables, I supplied ad hoc datasets, data analysis, reports, and interactive dashboards on inventory management, customer success, and payments.

• Created CSV files and used Excel templates, macros, pivot tables, and functions to report progress from offshore to management.

• Worked on Tableau Desktop to create dashboards, graphs, summaries, and calculated expressions.

• Imported Excel sheets, CSV, delimited data, and advanced Excel features into Tableau as per business needs.

• Applied Tableau features including calculated fields, parameters, table calculations, data blending, advanced calculations, and demographic and geographic fields for creating visualizations.

• Developed statistical process control techniques and readiness reporting standards by conducting data analysis and trend interpretation.

• Managed all User Acceptance Testing (UAT) deliverables to completion in accordance with requirements.

• Set up test data and functionalities to be evaluated as per technical specifications in the test environment. Environment: SQL, Python, Machine Learning, Excel, Tableau, MYSQL, Hive, Spark, Agile, Power BI, Power Query, Star Schema, AWS, AWS S3, AWS EMR

Pragmatic Tech Soft Pvt. Ltd Aug 2018 – Jan 2020

Role: Program Analyst

Responsibilities:

• Created comprehensive Tableau scorecards and dashboards using stack bars, bar graphs, scatter plots, geographical maps, heat maps, bullet charts, and Gantt charts to demonstrate key information for decision making.

• Proficiency in developing and overseeing pipelines, datasets, linked services, and data flows is required for Azure Data Factory (ADF).

• Sophisticated ETL pipelines were designed and put into place in Azure Data Factory to load, convert, and ingest data from a variety of on-premises and cloud sources into data lakes and warehouses.

• Developed integration runtimes, linked services, and datasets to process and integrate data from many data stores.

• Data transformations, such as aggregations, joins, and data type conversions, were conducted using ADF mapping data flows.

• Developed and scheduled data pipelines to automate data workflows, ensuring timely and accurate data delivery.

• Implemented error handling and retry mechanisms to manage data pipeline failures and ensure data integrity.

• Integrated Azure Data Factory with other Azure services, such as Azure Synapse Analytics and Azure Data Lake Storage, for seamless data processing and storage.

• Using Azure Synapse Analytics, I designed and constructed end-to-end data warehousing solutions that integrated data from multiple sources into a central repository.

• For effective data querying and analytics, serverless and dedicated SQL pools were created and managed.

• Synapse pipelines were created to automate data loading, transformation, and ingestion from cloud and on-premises sources during ETL operations.

• Used Apache Spark pools to manage large amounts of data, performing intricate aggregations and transformations.

• ADLS was used in the architecture and management of data lake solutions, allowing for the safe and scalable storage of both structured and unstructured data.

• Maintained compliance with data governance regulations and optimized storage costs through managed data lifecycle and retention policies.

• Azure Monitor, Log Analytics, and alerts were used to monitor data pipelines to track performance indicators and spot problems.

• Reduced downtime by performing root cause analysis and troubleshooting for pipeline issues.

• Designed and implemented HDFS architecture for efficient data storage and retrieval.

• Created and optimized Hive queries for data analysis and reporting, leveraging partitioning and indexing.

• Utilizing Hadoop for large-scale data storage, real-time data processing applications were developed utilizing HBase.

Environment: SQL, Python, Excel, Tableau, MYSQL, Hive Scripting, Spark, Agile, Power BI, Azure Data Factory, Power Query, Teradata, UNIX, JIRA, ERWIN, Data Quality, FTP, SFTP

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