Ashish Kumar Tatikunta
Austin,TX
Summary
Highly accomplished and results-oriented machine learning and artificial intelligence professional with 12+ years of progressive experience in developing, deploying, and scaling innovative AI solutions across diverse industries. Proven ability to lead cross-functional teams, drive research and development initiatives, and translate complex business requirements into impactful AI applications. Expertise in a wide range of machine learning algorithms, deep learning frameworks, and cloud-based AI platforms. Passionate about leveraging AI to solve challenging problems and drive business growth.
Overview
13
13
years of professional experience
Work History
Senior Principal Machine Learning Engineer
Open Text Technologies
Hyderabad, IN
11.2023 - 02.2025
- Led the research, design, and development of cutting-edge machine learning models for [ predictive maintenance, fraud detection, personalized recommendations]
- Spearheaded the development and deployment of scalable AI/ML pipelines on cloud platforms ( AWS, GCP), resulting in a [quantifiable achievement, 20% improvement in model performance, 15% reduction in operational costs]
- Mentored and guided a team of 5+ junior and mid-level machine learning engineers, fostering a collaborative and high-performing environment
- Collaborated closely with product management and engineering teams to define AI product roadmaps and translate business needs into technical specifications
- Evaluated and implemented new machine learning algorithms, tools, and frameworks to enhance model accuracy and efficiency
- Successfully delivered critical AI projects on time and within budget, contributing significantly to [increased customer engagement, improved operational efficiency]
- Developed and maintained comprehensive documentation for AI models, pipelines, and deployment processes
- Stayed abreast of the latest advancements in machine learning, deep learning, and AI technologies, and proactively explored their potential application within the organization
- Presented technical findings and project updates to both technical and non-technical stakeholders
- Contributed to the development of best practices and standards for machine learning development and deployment within the company
- Developed and deployed large language model-based applications using Python, reducing processing time by 30% through optimized fine-tuning and inference pipelines
- Designed machine learning models for [specific use case, e.g., predictive analytics, NLP], achieving [quantifiable result, 25% accuracy improvement]
- Integrated AI solutions with existing Java-based systems, ensuring seamless scalability and performance across microservices architectures
- Collaborated with cross-functional teams to define project requirements, mentor junior developers, and implement best practices in code quality and testing
- Lead the design and implementation of AI/ML systems, including large language models, to enhance [specific functionality, document processing, search, or customer insights] for enterprise applications
- Architect scalable, fault-tolerant solutions using Java and Python, integrating with cloud platforms (AWS) and microservices frameworks
- Define technical strategy and roadmaps for AI-driven features, collaborating with product managers, data scientists, and engineering teams
- Oversee end-to-end development lifecycle, from model training and deployment to production monitoring, ensuring high availability and performance
- Mentor senior and junior engineers, fostering a culture of technical excellence and innovation through code reviews and knowledge-sharing sessions
- Drive adoption of best practices in ML ops, including CI/CD pipelines, containerization (Docker/Kubernetes), and model versioning
- Partner with stakeholders to translate business needs into technical requirements, delivering solutions aligned with enterprise standards
- Spearheaded the deployment of a large language model-powered feature [e.g., intelligent search or automated content tagging], improving user productivity by 35%
- Reduced inference latency of ML models by 40% through optimization of Python-based pipelines and Java backend integration
Lead Machine Learning Engineer
Cognizant Tech Solutions
Hyderabad, IN
06.2018 - 10.2023
- Led the end-to-end development lifecycle of machine learning solutions, from data collection and preprocessing to model training, evaluation, and deployment for [natural language processing, computer vision, time series forecasting]
- Designed and implemented robust data pipelines for ingesting, cleaning, and transforming large-scale datasets from various sources
- Developed and deployed machine learning models using a variety of algorithms, including [regression, classification, clustering, deep learning architectures]
- Conducted rigorous model evaluation and validation to ensure high performance and generalization
- Collaborated with data engineers to optimize data storage and retrieval for efficient model training and inference
- Contributed to the development of internal tools and libraries to streamline the machine learning development process
- Researched and experimented with novel machine learning techniques to address complex business challenges
- Provided technical expertise and guidance to junior data scientists and analysts
- Presented research findings and project outcomes through reports and presentations
- Designed and implemented Python-based ML models and large language model solutions, enhancing [specific functionality, e.g., data analytics, customer engagement] for global clients
- Collaborated with cross-functional teams to architect scalable microservices deployed on [e.g., AWS], ensuring seamless integration with legacy systems
- Conducted code reviews and mentored junior developers, improving team productivity and code quality through Agile best practices
- Optimized system performance by refactoring complex codebases and automating processes with CI/CD pipelines and tools like Jenkins and Docker
- Re-engineered a Java application servicing [specific metric, e.g., 2M+ users], boosting performance by 35% through optimized algorithms and database queries
- Reduced deployment times by 40% by implementing a microservices architecture, enabling faster feature rollouts for enterprise clients
- Mentored a team of 5 engineers to successfully transition a legacy system to a cloud-native platform, cutting operational costs by 15%
Machine Learning Engineer
Google
Hyderabad, IN
03.2012 - 05.2018
- Participated in the development and implementation of machine learning models for [mention specific applications, e.g., customer churn prediction, marketing campaign optimization, anomaly detection]
- Assisted in the data collection, cleaning, and preprocessing of datasets for model training
- Implemented machine learning algorithms using libraries such as [mention specific libraries, e.g., scikit-learn, Weka]
- Contributed to the evaluation and fine-tuning of machine learning models
- Gained experience in deploying and monitoring machine learning models in production environments
- Collaborated with senior team members on various machine learning projects
- Developed basic scripts for data analysis and visualization
- Learned and applied fundamental concepts of machine learning and statistical modeling
- Designed, developed, and maintained features for Google Maps applications using Java to ensure scalability, performance, and reliability
- Implemented backend services for geospatial data handling, user interface integration, and map rendering functionalities
- Collaborated with cross-functional teams to understand feature requirements, ensure seamless integration, and optimize user experience
- Utilized Google Cloud Platform (GCP) tools such as Google Compute Engine, Cloud Storage, BigQuery, and Pub/Sub to build and deploy scalable solutions for processing map data
- Ensured high availability, fault tolerance, and optimal performance of backend services hosted on Google Cloud
- Implemented cloud-based data pipelines for processing large-scale geospatial data and integrated machine learning models to improve location-based services
- Conducted performance tuning, debugging, and optimization for backend APIs and geospatial data processing systems to reduce latency and enhance map interactions
- Implemented caching mechanisms to optimize API response times and improve the overall performance of location-based services
- Integrated third-party APIs and services (such as traffic data, satellite imagery, etc.) into the Google Maps platform to enrich map features and provide real-time updates
- Worked on APIs for route calculation, distance estimation, and geolocation services
- Collaborated with data scientists to integrate machine learning models into Google Maps for real-time traffic prediction, user behavior analysis, and route optimization
- Utilized Google Cloud Machine Learning tools such as AI Platform and TensorFlow to deploy and monitor models in production
- Followed best practices for software development, including version control (Git), unit testing, and code reviews to ensure clean, maintainable, and well-documented code
- Advocated for coding standards, continuous integration/continuous delivery (CI/CD) practices, and automated testing within the development lifecycle
- Worked in Agile teams, participated in sprint planning, daily standups, and retrospectives to ensure timely delivery of features and bug fixes
- Collaborated with product managers, UX/UI designers, and other engineers to enhance user-facing and backend functionalities
- Implemented security best practices for handling user data, ensuring compliance with data privacy regulations (e.g., GDPR)
- Developed features and APIs with a focus on secure access, data encryption, and authorization using OAuth and other security protocols
- Provided ongoing support and troubleshooting for production systems, ensuring the smooth operation of Google Maps services
- Diagnosed and resolved complex issues related to map data rendering, user interactions, and geospatial services
Education
Bachelor of Engineering - Computer Science
Vignan Institute of Technology And Science (JNTUH)
HYDERABAD09-2011
Skills
- Amazon Web Services (AWS)
- CI/CD pipelines
- Communication (written and verbal)
- Critical Thinking
- Deep Learning Frameworks
- Docker
- Experimental Design
- FastAPI
- Flask
- Google Cloud Platform (GCP)
- Hadoop
- Hypothesis Testing
- Image Classification
- Java
- Kubernetes
- Leadership
- Machine Learning Algorithms
- Matplotlib
- Mentoring
- Microsoft Azure
- Model Monitoring
- NoSQL
- Object Detection
- Problem-solving
- Python
- R
- REST APIs
- Seaborn
- Sentiment Analysis
- Spark
- SQL
- Statistical Modeling
- Tableau
- Teamwork
- Text Preprocessing
- Topic Modeling
Timeline
Senior Principal Machine Learning Engineer
Open Text Technologies
11.2023 - 02.2025
Lead Machine Learning Engineer
Cognizant Tech Solutions
06.2018 - 10.2023
Machine Learning Engineer
Google
03.2012 - 05.2018
Bachelor of Engineering - Computer Science
Vignan Institute of Technology And Science (JNTUH)
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