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I am Pushkar Patil, an AI/ML Engineer with 3+ years delivering machine learning and data engineering solutions across ITSM, cloud, and large-scale platforms. I specialize in NLP (NER, sentiment analysis, QA systems, LLMs) and predictive analytics, and I have a track record of building scalable ML pipelines, anomaly detection systems, and real-time analytics using Kubernetes, MLflow, Spark, and Kafka. I enjoy applying AWS, GCP, and Azure for high-throughput data engineering and deployment. In my recent roles, I have built production-ready NLP pipelines, implemented real-time anomaly detection, and enabled AI-driven workflow automation across SaaS ecosystems, delivering measurable improvements in accuracy, reliability, and operational efficiency.…I am Pushkar Patil, an AI/ML Engineer with 3+ years delivering machine learning and data engineering solutions across ITSM, cloud, and large-scale platforms. I specialize in NLP (NER, sentiment analysis, QA systems, LLMs) and predictive analytics, and I have a track record of building scalable ML pipelines, anomaly detection systems, and real-time analytics using Kubernetes, MLflow, Spark, and Kafka. I enjoy applying AWS, GCP, and Azure for high-throughput data engineering and deployment. In my recent roles, I have built production-ready NLP pipelines, implemented real-time anomaly detection, and enabled AI-driven workflow automation across SaaS ecosystems, delivering measurable improvements in accuracy, reliability, and operational efficiency.

Pushkar Patil

AI Engineer, Data Scientist, Developer, +2

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I am Pushkar Patil, an AI/ML Engineer with 3+ years delivering machine learning and data engineering solutions across ITSM, cloud, and large-scale platforms. I specialize in NLP (NER, sentiment analysis, QA systems, LLMs) and predictive analytics, and I have a track record of building scalable ML pipelines, anomaly detection systems, and real-time analytics using Kubernetes, MLflow, Spark, and Kafka. I enjoy applying AWS, GCP, and Azure for high-throughput data engineering and deployment. In my recent roles, I have built production-ready NLP pipelines, implemented real-time anomaly detection, and enabled AI-driven workflow automation across SaaS ecosystems, delivering measurable improvements in accuracy, reliability, and operational efficiency.…I am Pushkar Patil, an AI/ML Engineer with 3+ years delivering machine learning and data engineering solutions across ITSM, cloud, and large-scale platforms. I specialize in NLP (NER, sentiment analysis, QA systems, LLMs) and predictive analytics, and I have a track record of building scalable ML pipelines, anomaly detection systems, and real-time analytics using Kubernetes, MLflow, Spark, and Kafka. I enjoy applying AWS, GCP, and Azure for high-throughput data engineering and deployment. In my recent roles, I have built production-ready NLP pipelines, implemented real-time anomaly detection, and enabled AI-driven workflow automation across SaaS ecosystems, delivering measurable improvements in accuracy, reliability, and operational efficiency.

Available to hire

I am Pushkar Patil, an AI/ML Engineer with 3+ years delivering machine learning and data engineering solutions across ITSM, cloud, and large-scale platforms. I specialize in NLP (NER, sentiment analysis, QA systems, LLMs) and predictive analytics, and I have a track record of building scalable ML pipelines, anomaly detection systems, and real-time analytics using Kubernetes, MLflow, Spark, and Kafka. I enjoy applying AWS, GCP, and Azure for high-throughput data engineering and deployment.

In my recent roles, I have built production-ready NLP pipelines, implemented real-time anomaly detection, and enabled AI-driven workflow automation across SaaS ecosystems, delivering measurable improvements in accuracy, reliability, and operational efficiency.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI/ML Engineer at ServiceNow

February 1, 2025 - November 25, 2025

Architected and productionized NLP pipelines (NER, sentiment analysis, QA) leveraging transformer-based LLMs, enabling multi-intent classification and entity extraction at scale, resulting in a 28% uplift in ticket classification accuracy. Devised predictive analytics ecosystems by integrating XGBoost/LightGBM ensembles with time-series forecasting models, identifying ITSM anomalies and reducing downtime by 22%. Operationalized BERT, T5, and domain-adapted LLMs into ServiceNow’s intelligent search layer with relevance feedback loops, enhancing contextual retrieval precision by 30%. Architected fully containerized MLOps pipelines on Kubernetes, orchestrated via MLflow/Kubeflow for automated retraining, version lineage tracking, and zero-downtime model rollouts, compressing release cycles by 40%. Implemented real-time anomaly detection pipelines fusing Kafka log streams, Spark streaming analytics, and Redis in-memory state stores, achieving low-latency MTTR improvements for infrastruct

Data Analyst/Engineer at Experiential AI

August 1, 2024 - August 1, 2024

Streamlined 10+ enterprise workflows by embedding AI orchestration layers with n8n and Make.com, incorporating geohash-based location search for spatial automations and reclaiming 15 engineer hours/week. Engineered a hyperlocal environmental intelligence platform by ingesting sensor telemetry via GCP Pub/Sub, Flink Clusters, BigQuery, sustaining 1M+ records/minute throughput with millisecond-level SLAs. Constructed high-performance research data marts with geohash partitioning and location-based query acceleration on BigQuery and Spark SQL, enabling near real-time geoanalytics for 10M+ users. Delivered real-time & historical insights to government stakeholders using Looker dashboards powered by federated query engines, directly reducing reporting latency and operational cost by $10K/month.

Data Engineer I at Asfaliea

August 1, 2022 - August 1, 2022

Devised multi-source data ingestion (server logs, app telemetry, clickstream events) into AWS S3 via Airflow DAGs, generating unified analytical views and cutting manual ETL overhead by 25%. Built event-driven ETL workflows on AWS Glue, Lambda, Spark, and Snowflake Streams, scaling ingestion throughput by 15% while maintaining schema evolution guarantees. Directed cloud migration blueprinting IAM policy hierarchies, S3 tiered storage strategies, and re-platformed orchestration workflows, enhancing reliability metrics by 30%. Instituted validation frameworks with PyTest and CI/CD hooks to validate transformations, schemas, and anomaly detection eradicating 100+ recurring data defects in production. Enhanced Snowflake performance (clustering, pruning, caching) and built CloudWatch monitoring with SLA Airflow triggers and ML anomaly detection, accelerating sales forecasting and preventing revenue-impacting downtime.