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I’m an AI/ML Engineer who enjoys building production-ready machine learning and generative AI systems that solve real business problems. Over the past few years, I’ve worked across the full ML lifecycle—designing data pipelines, training and optimizing models, and deploying AI services—using Python, PySpark, Azure, Databricks, and GCP. I’m especially interested in LLM-powered applications, including RAG pipelines, tool/agent workflows, and evaluation/observability for reliability at scale. I’ve also built automation and data quality systems that improve retrieval relevance, reduce manual effort, and strengthen end-to-end data trust through monitoring, validation, and continuous improvement.…I’m an AI/ML Engineer who enjoys building production-ready machine learning and generative AI systems that solve real business problems. Over the past few years, I’ve worked across the full ML lifecycle—designing data pipelines, training and optimizing models, and deploying AI services—using Python, PySpark, Azure, Databricks, and GCP. I’m especially interested in LLM-powered applications, including RAG pipelines, tool/agent workflows, and evaluation/observability for reliability at scale. I’ve also built automation and data quality systems that improve retrieval relevance, reduce manual effort, and strengthen end-to-end data trust through monitoring, validation, and continuous improvement.

Aishwarya Sajjan

AI Engineer, Data Engineer, AI Developer, +2

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I’m an AI/ML Engineer who enjoys building production-ready machine learning and generative AI systems that solve real business problems. Over the past few years, I’ve worked across the full ML lifecycle—designing data pipelines, training and optimizing models, and deploying AI services—using Python, PySpark, Azure, Databricks, and GCP. I’m especially interested in LLM-powered applications, including RAG pipelines, tool/agent workflows, and evaluation/observability for reliability at scale. I’ve also built automation and data quality systems that improve retrieval relevance, reduce manual effort, and strengthen end-to-end data trust through monitoring, validation, and continuous improvement.…I’m an AI/ML Engineer who enjoys building production-ready machine learning and generative AI systems that solve real business problems. Over the past few years, I’ve worked across the full ML lifecycle—designing data pipelines, training and optimizing models, and deploying AI services—using Python, PySpark, Azure, Databricks, and GCP. I’m especially interested in LLM-powered applications, including RAG pipelines, tool/agent workflows, and evaluation/observability for reliability at scale. I’ve also built automation and data quality systems that improve retrieval relevance, reduce manual effort, and strengthen end-to-end data trust through monitoring, validation, and continuous improvement.

Available to hire

I’m an AI/ML Engineer who enjoys building production-ready machine learning and generative AI systems that solve real business problems. Over the past few years, I’ve worked across the full ML lifecycle—designing data pipelines, training and optimizing models, and deploying AI services—using Python, PySpark, Azure, Databricks, and GCP.

I’m especially interested in LLM-powered applications, including RAG pipelines, tool/agent workflows, and evaluation/observability for reliability at scale. I’ve also built automation and data quality systems that improve retrieval relevance, reduce manual effort, and strengthen end-to-end data trust through monitoring, validation, and continuous improvement.

Skills

Experience Level

Expert

Expert

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Expert

Expert

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Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Work Experience

AI Engineer at Freddie Mac (Client-Walmart)

June 1, 2026 - Present

Engineered generative AI applications using Python, LLM APIs, and RAG architectures by integrating enterprise data with embedding-based retrieval and vector search. Built intelligent LLM agents and tool-calling workflows to orchestrate multi-step tasks and automate 10+ business processes, reducing manual intervention by ~35%. Developed production-grade RAG pipelines covering ingestion, chunking, embeddings, semantic retrieval, prompt orchestration, and response generation, improving retrieval relevance by 30%+. Implemented LLM evaluation and observability across 5,000+ interactions to measure quality, groundedness, latency, and failure patterns, enabling continuous prompt and retrieval optimization. Productionized AI services using FastAPI, Docker, cloud infrastructure, and CI/CD pipelines to reduce release time by ~40% and support scalable enterprise workloads.

AI Engineer at GE Healthcare

February 1, 2026 - May 1, 2026

Developed and supported AI/ML-driven technical solutions using Python and cloud services, improving automation and reducing manual troubleshooting effort by 30%+ across assigned engineering activities. Built and maintained automated data and assessment workflows integrating Elasticsearch, Kibana, Logstash, and Filebeat to process 10,000+ log/event records and improve monitoring and evaluation efficiency. Designed and optimized Python-based automation scripts and data-processing workflows to reduce repetitive manual tasks by ~40% and accelerate issue identification and resolution across development and lab environments. Collaborated with technical teams to troubleshoot 50+ cloud/AI-related issues, improving project completion efficiency and providing hands-on guidance for AWS, Python, data engineering, and deployment workflows. Applied machine learning, data analytics, and cloud engineering concepts to support 5+ cloud-based workflows across compute, storage, networking, deployment, and

Data Engineer at Cognizant Technology Solutions, Bangalore, India

March 1, 2022 - June 1, 2024

Architected end-to-end ETL pipelines using Azure Data Factory and Databricks (PySpark) to ingest and transform PepsiCo consumer data from Teradata into ADLS Gen2, processing millions of records daily across global markets with datasets ranging from 100MB to 100GB+. Designed and implemented a Medallion Architecture (Bronze/Silver/Gold) using Delta Lake on Databricks, creating structured analytics-ready data layers supporting downstream reporting and machine learning use cases for 6+ business units. Built complex PySpark and Spark SQL transformations for large-scale Teradata migrations using surrogate key logic, SCD patterns, and partitioning strategies, reducing query execution time by ~35%. Implemented data quality and validation checks (reconciliation, null/duplicate checks, source-to-target validation) across 25+ pipelines achieving 99% data accuracy critical for downstream analytics and model training reliability. Orchestrated data movement using ADF Mapping Data Flows and Copy Acti