AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Senior AIML Engineer | Designing Enterprise GenAI & Agentic Systems | Data & ML Platform Engineering
I’m a 𝐒𝐞𝐧𝐢𝐨𝐫 𝐀𝐈𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 focused on building scalable 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 for real-world business applications. My journey started in large-scale 𝐝𝐚𝐭𝐚 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠, where I built distributed data systems and optimized pipelines for performance, cost, and reliability. Over time, I transitioned into 𝐌𝐋 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐆𝐞𝐧𝐀𝐈, working at the intersection of data infrastructure, backend systems, and 𝐋𝐋𝐌-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬. Currently, I’m building an enterprise AI product that enables organizations to design, customize, and manage agent-based workflows across diverse use cases. My work includes 𝐦𝐮𝐥𝐭𝐢-𝐚𝐠𝐞𝐧𝐭 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧, 𝐑𝐀𝐆 (Retrieval-Augmented Generation) systems, tool integration frameworks, and secure multi-tenant system design.I design robust authentication and authorization (RBAC) models, including role hierarchies and fine-grained, policy-based access control to ensure secure and governed AI usage. I also focus on observability, cost optimization, and reliability for production 𝐋𝐋𝐌 𝐰𝐨𝐫𝐤𝐥𝐨𝐚𝐝𝐬. Core strengths: • Generative AI & Agentic Systems • RAG-based LLM Applications • Multi-tenant system design • Authentication & Authorization (RBAC, policy-based access control) • Distributed data & ML infrastructure • Backend engineering with Python & FastAPI • Cloud-native AI deployment With a strong foundation in data systems and backend engineering, I approach GenAI from a systems perspective — prioritizing scalability, governance, reliability, and operat
Ahalia School of Engineering and Technology, Palakkad
Bachelor of Technology - BTech, Computer Science
January 1, 2018 – July 1, 2021
Tiger Analytics
Senior AIML Engineer
January 1, 2026 – Present
Tiger Analytics
Machine Learning Engineer
October 1, 2024 – February 1, 2026
IQVIA
Associate Software Developer
August 1, 2021 – October 1, 2024
Banglore
Big Data Migration Project
August 1, 2021 – Present
Led a successful migration project transitioning from Mainframe to cutting-edge Big Data Technologies. Employed Hadoop, HDFS, Apache Spark (Spark-SQL, PySpark), Python, SQL, Scala, MySQL, and Airflow for seamless job scheduling. Executed tasks with agility through Agile methodologies, leveraging Jenkins, Jira, and Gitlab for streamlined collaboration and version control.
Baseline: Infrastructure
Qwiklabs
June 23, 2026 – Present
Data Science Orientation
Coursera
June 23, 2026 – Present
Build a Face Recognition Application using Python as a part of AI-For-India Event
HCL GUVI
June 23, 2026 – Present
Python for Data Science and Machine Learning Bootcamp
Udemy
June 23, 2026 – Present
Getting Started: Create and Manage Cloud Resources
Qwiklabs
June 23, 2026 – Present
DevOps Essentials
Qwiklabs
June 23, 2026 – Present
Cloud Engineering
Qwiklabs
June 23, 2026 – Present
Kubernetes in Google Cloud
Qwiklabs
June 23, 2026 – Present
Networking in the Google Cloud
Qwiklabs
June 23, 2026 – Present
Getting Started with SAS Programming
Coursera
June 23, 2026 – Present
Google Cloud Essentials
Qwiklabs
June 23, 2026 – Present
Using Python to Access Web Data
Coursera
June 23, 2026 – Present
HTML, CSS, and Javascript for Web Developers
Coursera
June 23, 2026 – Present
Crash Course on Python
Coursera
June 23, 2026 – Present
Python Data Structures
Coursera
June 23, 2026 – Present
Programming for Everybody (Getting Started with Python)
Coursera
June 23, 2026 – Present
PYTHON (Basic)
Team HackerRank
June 23, 2026 – Present
Data Visualization with Python
Coursera
June 23, 2026 – Present
Professional Data Engineer Certification
June 23, 2026 – Present
Databricks Certified Machine Learning Associate
Databricks
June 23, 2026 – Present
Databricks Certified Data Engineer Professional
Databricks
June 23, 2026 – Present
Azure Databricks & Spark For Data Engineers (PySpark / SQL)
Udemy
June 23, 2026 – Present
𝐈𝐁𝐌 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐞
IBM
June 23, 2026 – Present
Microsoft Certified: Azure Data Fundamentals
Microsoft
June 23, 2026 – Present
Microsoft Certified: Azure Fundamentals
Microsoft
June 23, 2026 – Present
Machine Learning with Python
Coursera
June 23, 2026 – Present
Partner Training - Financial Services Industry Specialization for Gen AI & LLM
Databricks
June 23, 2026 – Present
Data Analysis with Python
Coursera
June 23, 2026 – Present
Databases and SQL for Data Science with Python
Coursera
June 23, 2026 – Present
Python Project for Data Science
Coursera
June 23, 2026 – Present
Python for Data Science, AI & Development
Coursera
June 23, 2026 – Present
Data Science Methodology
Coursera
June 23, 2026 – Present
Tools for Data Science
Coursera
June 23, 2026 – Present
What is Data Science ?
Coursera
June 23, 2026 – Present
Cultural Fit Analysis
The candidate's project and work experience demonstrate a strong focus on data engineering and machine learning, which aligns well with a data-driven culture. The breadth of certifications across various platforms (Databricks, Google, Azure, IBM) indicates a proactive learning attitude and adaptability. However, the target role is 'Data Analyst', while the experience leans heavily towards 'Data Engineer' and 'Machine Learning Engineer'. While there's overlap, a pure Data Analyst role might require more emphasis on business intelligence, reporting, and specific analytical tools beyond core data processing, which is less explicitly highlighted. The project diversity is limited to one detailed project.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight collaboration in fast-paced Agile environments and a focus on translating business logic into scalable workflows, indicating good operational fit and teamwork potential. The quantifiable results (e.g., 40% reduction in operational costs, 25% improvement in data accuracy) suggest a results-oriented approach.