Rakesh R
Principal AI & Cloud Architect
Bangalore
Summary
With over 11 years in the IT domain, I have evolved from a Cloud Support Engineer to a Cloud Specialist, specializing in LLM and Generative AI. I have a comprehensive understanding of cloud ecosystems, architecting resilient Cloud, Security, and Generative AI solutions. I have deep expertise in advanced analytics and predictive algorithm development, leveraging Machine Learning to solve complex business challenges. My approach emphasizes scalability, data integrity, ethical AI practices, and team collaboration.
Overview
12
12
years of professional experience
3
3
Certifications
3
3
Languages
Work History
Principal AI & CLoud Architect
HCL Technologies
02.2021 - Current
- Developed and fine-tuned state-of-the-art LLMs, ensuring optimal performance across diverse applications.
- Implemented Generative AI techniques to enhance model generalization and scalability.
- Designed robust evaluation protocols, achieving consistently high benchmarks in model performance.
- Mentored junior team members, transferring knowledge from extensive LLM and Generative AI experience.
- Collaborated cross-functionally to align AI goals with product deliverables, ensuring seamless integrations.
- Led deployment strategies, ensuring models were scalable in cloud environments like AWS and GCP.
- Stayed at the forefront of LLM and Generative AI advancements through continuous learning, workshops, and seminars.
- Spearheaded ethical AI initiatives, conducting regular bias assessments and ensuring model fairness.
- Optimized model performance using hyperparameter tuning, maximizing efficiency in both batch and real-time processing.
- Documented model development processes, ensuring clear and actionable insights for stakeholders.
- Managed data normalization and preprocessing, maintaining dataset quality for optimal model training.
- Fostered feedback mechanisms, iterating on model performance based on real-world feedback.
- Drove R&D initiatives exploring novel AI techniques, positioning the team at the forefront of AI innovation.
- Collaborated with hardware teams for optimal resource utilization during model training.
- Regularly reported on AI progress, challenges, and solutions, facilitating informed decision-making for leadership.
- Integrated models into product environments, focusing on user-centric outcomes and seamless interactions.
- Championed continuous improvement, attending training to further refine expertise in Generative AI.
- Led domain-specific model adaptation projects, catering to niche markets and user segments.
- Managed collaborations with data teams, ensuring a consistent supply of relevant datasets.
- Designed and implemented multi-task learning techniques, broadening model capabilities and market relevance.
- Developed and fine-tuned state-of-the-art LLMs, ensuring optimal performance across diverse applications.
- Implemented Generative AI techniques to enhance model generalization and scalability.
- Designed robust evaluation protocols, achieving consistently high benchmarks in model performance.
- Mentored junior team members, transferring knowledge from extensive LLM and Generative AI experience.
- Collaborated cross-functionally to align AI goals with product deliverables, ensuring seamless integrations.
- Led deployment strategies, ensuring models were scalable in cloud environments like AWS and GCP.
- Stayed at the forefront of LLM and Generative AI advancements through continuous learning, workshops, and seminars.
- Spearheaded ethical AI initiatives, conducting regular bias assessments and ensuring model fairness.
- Optimized model performance using hyperparameter tuning, maximizing efficiency in both batch and real-time processing.
- Documented model development processes, ensuring clear and actionable insights for stakeholders.
- Managed data normalization and preprocessing, maintaining dataset quality for optimal model training.
- Fostered feedback mechanisms, iterating on model performance based on real-world feedback.
- Drove R&D initiatives exploring novel AI techniques, positioning the team at the forefront of AI innovation.
- Collaborated with hardware teams for optimal resource utilization during model training.
- Regularly reported on AI progress, challenges, and solutions, facilitating informed decision-making for leadership.
- Integrated models into product environments, focusing on user-centric outcomes and seamless interactions.
- Championed continuous improvement, attending training to further refine expertise in Generative AI.
- Led domain-specific model adaptation projects, catering to niche markets and user segments.
- Managed collaborations with data teams, ensuring a consistent supply of relevant datasets.
- Designed and implemented multi-task learning techniques, broadening model capabilities and market relevance.
Senior Data Scientist & Cloud Specialist
Vsoft Consulting
01.2021 - Current
- Data Modeling: Utilized TensorFlow and Scikit-learn for statistical analyses and ML modeling.
- Cloud Deployment: Orchestrated ML deployments on AWS, Azure, and GCP using Docker and Kubernetes.
- Feature Optimization: Applied techniques like PCA and LDA for model performance boost.
- Team Collaboration: Integrated ML models using CI/CD pipelines; liaised with DevOps.
- Model Tuning: Employed grid search and Bayesian optimization for hyperparameter refinement.
- Visualization: Crafted dashboards using Tableau and Power BI, translating data into insights.
- Deep Learning: Fine-tuned architectures such as CNNs and RNNs using GPU acceleration.
- Research: Stayed updated with ML advancements, integrating novel methodologies.
- Cloud Solutions: Developed microservices with AWS Lambda and Azure Functions.
- Security: Ensured data integrity with encryption, audits, and compliance measures.
- Data Pipelines: Managed real-time processing using Kafka and Spark.
- Database Management: Handled data using PostgreSQL, MongoDB, and Cassandra.
- Experimentation: Conducted A/B tests and uplift modeling for model validation.
- Automation: Streamlined tasks and processes using Python automation.
- Documentation: Authored tech docs, including model architecture and API documentation using tools like Swagger.
Data Engineer & Cloud Associate
Caterpillar Inc (Payroll EGS)
11.2017 - 04.2018
- Architected and implemented ETL (Extract, Transform, Load) processes for big data ingestion into data lakes and warehouses.
- Utilized cloud platforms (e.g., AWS, Azure, GCP) to design scalable and cost-effective data solutions.
- Implemented real-time data streaming solutions using technologies like Apache Kafka and Apache Flink.
- Collaborated with DevOps teams to automate and enhance CI/CD pipelines for data applications.
- Optimized SQL and NoSQL database performance, ensuring efficient querying and robust data integrity.
- Spearheaded the integration of machine learning models into production using tools like MLflow or TensorFlow Serving.
- Led data infrastructure security audits and implemented best practices in data encryption, masking, and redaction.
- Facilitated containerization of data services using Docker and Kubernetes orchestration.
- Drove data governance initiatives, ensuring compliance with GDPR, CCPA, and other regulatory standards.
- Championed the adoption of Infrastructure as Code (IaC) methodologies using Terraform or CloudFormation.
Machine Learning Engineer
HCL Technologies
06.2014 - 09.2017
- Developed, optimized, and validated predictive and prescriptive machine learning models using libraries like Scikit-learn, TensorFlow, and PyTorch.
- Interfaced with business analysts to translate requirements into robust ML algorithms.
- Utilized distributed computing frameworks such as Apache Spark for processing large datasets.
- Implemented model versioning and monitoring using tools like DVC or ModelDB.
- Collaborated in A/B testing initiatives, evaluating model performance in real-world scenarios.
- Drove the automation of feature engineering, leveraging techniques for automated feature selection and extraction.
- Spearheaded deep learning projects, specializing in areas like NLP (Natural Language Processing) or computer vision.
- Ensured model fairness, interpretability, and robustness using techniques like SHAP or LIME.
- Collaborated with DevOps to containerize and deploy ML models using tools like Docker and Kubernetes.
- Optimized GPU and TPU resource utilization for deep learning workloads.
Cloud Support Engineer
Self Employed
06.2012 - 09.2014
- Offered 24/7 technical support for cloud infrastructure issues, ensuring optimal uptime and SLA adherence.
- Configured and managed cloud services such as EC2, S3, or Cloud SQL, optimizing for performance and cost.
- Implemented cloud-native monitoring tools like CloudWatch, Stackdriver, or Azure Monitor.
- Collaborated in the migration of legacy applications to microservices-based architectures in the cloud.
- Assisted in the setup and maintenance of VPC, subnetting, and firewall configurations for cloud environments.
- Handled incident management, root cause analysis, and post-mortem reviews for cloud-related outages.
- Supported the DevOps team in CI/CD pipeline setup using Jenkins, GitLab CI, or Travis CI.
- Conducted performance tuning and optimization of applications in cloud environments.
- Assisted in IAM (Identity and Access Management) configurations, ensuring role-based access controls.
- Stayed abreast of cloud technology updates and recommended adoption of new features for infrastructure optimization.
Education
MBA - Business Analytics
Alliance University
Bangalore 04.2001 -
B.E - Electronics & Communications
VTU University
Tumkur,Karnataka 04.2001 -
Skills
Frameworks & Architectures: OpenAI, Transformers, TensorFlow, PyTorch, Keras, GANs
Model Development: LLMs, Fine-tuning, Zero/Few-shot learning, Seq2Seq, Meta-learning, Reinforcement learning
Cloud & Infrastructure: AWS, Azure, GCP, Docker, Kubernetes, Terraform, Ansible
Data Management: Vector embedding, Big Data (Hadoop, Spark), ETL, SQL, NoSQL
Deployment & Integration: Triton, REST APIs, Microservices, CI/CD, DevOps
Security & Ethics: Cybersecurity, Encryption, VPNs, Firewalls, Model bias
Optimization & Analysis: Scalability, Inference optimization, Hyperparameter tuning
Programming: Python, R, SQL, NoSQL, Git
Visualization & Reporting: Tableau, Power BI, Plotly, Metrics
Algorithms: Regression, kNN, SVM, Decision Trees, Random Forest, NLP, Deep Learning
Methodologies: Large-scale training, Cloud Networking, Agile
Certification
Microsoft Azure Solution Architect Expert
Timeline
Principal AI & CLoud Architect
HCL Technologies
02.2021 - Current
Senior Data Scientist & Cloud Specialist
Vsoft Consulting
01.2021 - Current
Data Engineer & Cloud Associate
Caterpillar Inc (Payroll EGS)
11.2017 - 04.2018
Machine Learning Engineer
HCL Technologies
06.2014 - 09.2017
Cloud Support Engineer
Self Employed
06.2012 - 09.2014
MBA - Business Analytics
Alliance University
04.2001 -
B.E - Electronics & Communications
VTU University
04.2001 -