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Hi, I’m Pushkar Patil, an AI/ML Engineer with 3+ years of experience delivering ML 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 enjoy turning complex data into accessible insights. I’m proficient with Kubernetes, MLflow, Spark, Kafka, and cloud platforms (AWS, GCP, Azure), building scalable pipelines and real-time analytics. I’m passionate about delivering reliable AI-powered solutions, collaborating with cross-functional teams to deploy production-grade models, monitor performance, and iterate quickly to drive impact across customer service, IT operations, and business analytics.…Hi, I’m Pushkar Patil, an AI/ML Engineer with 3+ years of experience delivering ML 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 enjoy turning complex data into accessible insights. I’m proficient with Kubernetes, MLflow, Spark, Kafka, and cloud platforms (AWS, GCP, Azure), building scalable pipelines and real-time analytics. I’m passionate about delivering reliable AI-powered solutions, collaborating with cross-functional teams to deploy production-grade models, monitor performance, and iterate quickly to drive impact across customer service, IT operations, and business analytics.

Pushkar Patil

AI Engineer, Data Scientist, Full Stack Developer, +2

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Hi, I’m Pushkar Patil, an AI/ML Engineer with 3+ years of experience delivering ML 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 enjoy turning complex data into accessible insights. I’m proficient with Kubernetes, MLflow, Spark, Kafka, and cloud platforms (AWS, GCP, Azure), building scalable pipelines and real-time analytics. I’m passionate about delivering reliable AI-powered solutions, collaborating with cross-functional teams to deploy production-grade models, monitor performance, and iterate quickly to drive impact across customer service, IT operations, and business analytics.…Hi, I’m Pushkar Patil, an AI/ML Engineer with 3+ years of experience delivering ML 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 enjoy turning complex data into accessible insights. I’m proficient with Kubernetes, MLflow, Spark, Kafka, and cloud platforms (AWS, GCP, Azure), building scalable pipelines and real-time analytics. I’m passionate about delivering reliable AI-powered solutions, collaborating with cross-functional teams to deploy production-grade models, monitor performance, and iterate quickly to drive impact across customer service, IT operations, and business analytics.

Available to hire

Hi, I’m Pushkar Patil, an AI/ML Engineer with 3+ years of experience delivering ML 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 enjoy turning complex data into accessible insights. I’m proficient with Kubernetes, MLflow, Spark, Kafka, and cloud platforms (AWS, GCP, Azure), building scalable pipelines and real-time analytics.

I’m passionate about delivering reliable AI-powered solutions, collaborating with cross-functional teams to deploy production-grade models, monitor performance, and iterate quickly to drive impact across customer service, IT operations, and business analytics.

Skills

Experience Level

Expert

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Work Experience

AI/ML Engineer at ServiceNow

February 1, 2025 - November 11, 2025

Architected and productionized advanced 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 in

Data Analyst/Engineer at Experiential AI

August 1, 2024 - August 1, 2024

Streamlined 10+ enterprise workflows by embedding AI-powered orchestration layers with n8n and Make.com, eliminating repetitive manual dependencies 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 on BigQuery, augmented with Spark SQL pipelines for distributed preprocessing, enabling near real-time analytics for 10M+ end 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.