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AI/ML Engineer with 5+ years building and deploying machine learning systems and production data pipelines across financial services, healthcare, and e-commerce. I specialize in Python, MLOps, and scalable cloud deployments, integrating models into reliable APIs and data workflows. I collaborate with software, product, and data teams to ship, monitor, and retrain models in production—using Docker, Kubernetes, and model monitoring to improve reliability, availability, and reduce data incidents and operational cost.…AI/ML Engineer with 5+ years building and deploying machine learning systems and production data pipelines across financial services, healthcare, and e-commerce. I specialize in Python, MLOps, and scalable cloud deployments, integrating models into reliable APIs and data workflows. I collaborate with software, product, and data teams to ship, monitor, and retrain models in production—using Docker, Kubernetes, and model monitoring to improve reliability, availability, and reduce data incidents and operational cost.

Akhila Neela

AI Engineer, AI Developer, Data Scientist, +2

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AI/ML Engineer with 5+ years building and deploying machine learning systems and production data pipelines across financial services, healthcare, and e-commerce. I specialize in Python, MLOps, and scalable cloud deployments, integrating models into reliable APIs and data workflows. I collaborate with software, product, and data teams to ship, monitor, and retrain models in production—using Docker, Kubernetes, and model monitoring to improve reliability, availability, and reduce data incidents and operational cost.…AI/ML Engineer with 5+ years building and deploying machine learning systems and production data pipelines across financial services, healthcare, and e-commerce. I specialize in Python, MLOps, and scalable cloud deployments, integrating models into reliable APIs and data workflows. I collaborate with software, product, and data teams to ship, monitor, and retrain models in production—using Docker, Kubernetes, and model monitoring to improve reliability, availability, and reduce data incidents and operational cost.

Available to hire

AI/ML Engineer with 5+ years building and deploying machine learning systems and production data pipelines across financial services, healthcare, and e-commerce. I specialize in Python, MLOps, and scalable cloud deployments, integrating models into reliable APIs and data workflows.

I collaborate with software, product, and data teams to ship, monitor, and retrain models in production—using Docker, Kubernetes, and model monitoring to improve reliability, availability, and reduce data incidents and operational cost.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Work Experience

AI/ML Engineer at Cognizant

December 1, 2023 - Present

Built and deployed ML-driven data pipelines on Databricks integrating XGBoost and SHAP-based models into production services via FastAPI; reduced data incidents by 87% through pre-write schema assertions and validation. Designed containerized MLOps workflows using Docker and Kubernetes on Azure AKS, coordinating with software and product teams to integrate models into cloud services; provisioned Service Principals and Key Vault secret scopes for secure credential management. Developed LangChain-based RAG pipelines and monitored model outputs in production, working cross-functionally to resolve issues and improve reliability. Established GitHub Actions CI/CD for model and pipeline deployments; mentored mid-level engineers on production ML best practices and code review standards.

Data Engineer at Deloitte

June 1, 2020 - December 31, 2022

Built production-grade PySpark ETL pipelines feeding downstream ML and analytics systems, processing millions of records daily; reduced data latency by 40% and pipeline runtime by 25%. Designed medallion architecture (bronze/silver/gold) with grain-aware SQL and SCD Type 2 supporting model feature stores. Delivered $1.1M cost savings through compute-aware cluster sizing and optimizations. Architected Kafka streaming pipelines supplying real-time features to downstream models, cutting data freshness lag from hours to seconds and enforcing quality checks preventing null/duplicate propagation. Maintained Jenkins/Docker/Kubernetes CI/CD for data and model services; authored incident post-mortems and coordinated handovers with Data Science teams, improving delivery velocity by 45%.