May 2021 – Oct 2023 · 2 yrs 5 mos

• Built Advance Analytics Solutions for Customer OPS
• Engineered scalable DL architectures and NLP models for a Global Pharmaceutical leader to automate Adverse Event (AE) of Drug identification in Medical Triage and Pharmacovigilance.
• Deployed Speech-to-Text (STT) and NLU frameworks to extract real-time actionable insights from patient interactions for medical safety decision support.
• Solved problems using descriptive and predictive analytics.
• Applyed robust, efficient and scalable deep learning based solutions on real-time customer data for extraction of business value through insights and recommendations.
• Replaced a lot of manual effort to bring automation wherever necessary and possible through AI.
• Automated manual medical auditing workflows through AI-driven solutions, delivering a 200% ROI while enhancing data integrity for complex regulatory compliance.- Using Deep learning based solutions to build robust models , helping to provide robust client solutions .
• Enhancing the capabilities to greater heights through working in versatile challenges in speech and text technology.
• Configured and managed multi-GPU clusters (NVIDIA A100/H100) using Distributed Data Parallel (DDP) strategies to optimize Large Language Model (LLM) training efficiency.
• Orchestrated end-to-end environment setup and resource allocation for deep learning workloads, ensuring 99.9% uptime for mission-critical Drug Safety monitoring pipelines.
• Led cross-functional teams to transition experimental AI research into "Production-First" scalable healthcare applications for real-time clinical use.

Jul 2020 – May 2021 · 10 mos · Bangalore Urban, Karnataka, India

• Customer Success Engineering
• ● Established a Quality-driven approach to delivering excellent customer satisfaction by ensuring three key principles are in place:
• Understand | Empathize | Solve
• ● Achieved 100% customer satisfaction score in consecutive quarters.
• ● Able to achieve 99% SLA for resolving customer issues on connectors.
• ● Built data pipelines on the cloud, dbt, data flow, and model data warehousing for enterprise customers ensuring a successful transition from on-premise to cloud.
• ● Experience in CustomerFocused problem solving on Cloud-based ETL platform for end-to-end solutions.
• ● Consulting customers about the best sources to choose from based on their volume, variety and complexity of data.
• ● provided efficient solutions by setting up the right pipeline for the right data.
• ● Worked on various modern sources of data pipelines such as Redshift, Postgres, Big-query, asana, Hubspot, Facebook ads, google ads, AWS S3, cloud files etc.
• ● Expertise in simplifying product complexity and challenges through the abstraction of too many technical details of products and features to help customers have a smooth product experience.

Dec 2018 – Mar 2021 · 2 yrs 3 mos · Bangalore

• CellStrat is India’s leading consulting and training firm in emerging areas of Artificial Intelligence and Deep Learning. This position is an non-paid internship + academic AI research program.
• At CellStrat I have learned about being a searcher of technology. I later applied my research skills for Machine Learning and Deep Learning and NLP domain.
• Expertise in end to end product building and deployment on AWS.
• Developed several NLP based solutions such as Topic Modeling, Text Summarization, Key word generation, question answering etc and deployed on production ready servers.
• Worked on research paper publication and structuring .
• Learned to develop and apply research paper based solutions on public data.
• Won several hackathons and competitions on ML and deep learning based problems.
• Experience training data scientists on applying basic to advance NLP based solutions.
• Organized and hosted many webinars on all topics around AI, DL and NLP.

Apr 2017 – Jul 2020 · 3 yrs 3 mos

• Engineered ETL data pipelines with PowerCenter and IDQ transformations, processing enterprise-scale volumes to deliver 99%+ data accuracy for client analytics.
• Automated CI/CD for IDQ jobs via GitHub + Jenkins, slashing deployment time by 70% and enabling zero-downtime releases.
• Resolved complex IDQ/AWS production issues through log parsing and performance tuning, minimizing MTTR to under 2 hours.
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• Built Cloud Data Quality microservices for real-time data profiling, cleansing, and observability in hybrid environments.
• ​* Deployed ML-driven rules (bigrams, edit distance, tokenization, regex) for deduplication, null handling, and error correction on terabyte datasets.
• Engineered probabilistic fuzzy matching to correlate fields across sources, boosting entity resolution accuracy to 95%+ for master data management.
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• Integrated sentiment analysis on customer logs/comms to trigger business alerts, reducing churn risks by surfacing issues proactively.
• Authored DQ training modules for Informatica University and onboarded 20+ engineers, standardizing best practices.
• Mentored teams on ETL fundamentals, accelerating ramp-up and cross-functional delivery.
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Sep 2012 – Apr 2017 · 4 yrs 7 mos

• This role begins my journey into data, data engineering and ETL fundamentals.
• Learned, developed and gained experience in core ETL architecture, applying os ( Linux, windows) fundamentals, database concepts and file handling.
• strong experience in writing efficient and complex SQL queries for enterprise customers using Informatica Power Center.
• Developed a great understanding of end-to-end data pipelines, different source systems, and engineering best practices for data warehousing.
• Gained experience working on data warehousing, OLTP and OLAP-based systems for enterprise customers.