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I'm a Senior AI/ML Engineer with 9+ years of designing and delivering ML, Generative AI, and data analytics solutions across financial services, healthcare, retail, and enterprise domains. I have hands-on experience building end-to-end ML systems, including data ingestion, feature engineering, model training, deployment, monitoring, and continuous improvement in production. I specialize in applying deep learning frameworks (TensorFlow, PyTorch, Keras) for classification, forecasting, NLP, and GenAI applications. I work with retrieval-augmented generation (RAG), semantic search, embeddings, and vector databases, leveraging LangChain and related tools. Proficient across AWS, Azure, and GCP, I implement scalable MLOps with MLflow, SageMaker, and container orchestration (Docker, Kubernetes), while collaborating with cross-functional teams to translate complex business needs into compliant, production-ready AI solutions.…I'm a Senior AI/ML Engineer with 9+ years of designing and delivering ML, Generative AI, and data analytics solutions across financial services, healthcare, retail, and enterprise domains. I have hands-on experience building end-to-end ML systems, including data ingestion, feature engineering, model training, deployment, monitoring, and continuous improvement in production. I specialize in applying deep learning frameworks (TensorFlow, PyTorch, Keras) for classification, forecasting, NLP, and GenAI applications. I work with retrieval-augmented generation (RAG), semantic search, embeddings, and vector databases, leveraging LangChain and related tools. Proficient across AWS, Azure, and GCP, I implement scalable MLOps with MLflow, SageMaker, and container orchestration (Docker, Kubernetes), while collaborating with cross-functional teams to translate complex business needs into compliant, production-ready AI solutions.

Nikhil Nandala

AI Engineer, Data Scientist, AI Developer, +2

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I'm a Senior AI/ML Engineer with 9+ years of designing and delivering ML, Generative AI, and data analytics solutions across financial services, healthcare, retail, and enterprise domains. I have hands-on experience building end-to-end ML systems, including data ingestion, feature engineering, model training, deployment, monitoring, and continuous improvement in production. I specialize in applying deep learning frameworks (TensorFlow, PyTorch, Keras) for classification, forecasting, NLP, and GenAI applications. I work with retrieval-augmented generation (RAG), semantic search, embeddings, and vector databases, leveraging LangChain and related tools. Proficient across AWS, Azure, and GCP, I implement scalable MLOps with MLflow, SageMaker, and container orchestration (Docker, Kubernetes), while collaborating with cross-functional teams to translate complex business needs into compliant, production-ready AI solutions.…I'm a Senior AI/ML Engineer with 9+ years of designing and delivering ML, Generative AI, and data analytics solutions across financial services, healthcare, retail, and enterprise domains. I have hands-on experience building end-to-end ML systems, including data ingestion, feature engineering, model training, deployment, monitoring, and continuous improvement in production. I specialize in applying deep learning frameworks (TensorFlow, PyTorch, Keras) for classification, forecasting, NLP, and GenAI applications. I work with retrieval-augmented generation (RAG), semantic search, embeddings, and vector databases, leveraging LangChain and related tools. Proficient across AWS, Azure, and GCP, I implement scalable MLOps with MLflow, SageMaker, and container orchestration (Docker, Kubernetes), while collaborating with cross-functional teams to translate complex business needs into compliant, production-ready AI solutions.

Available to hire

I’m a Senior AI/ML Engineer with 9+ years of designing and delivering ML, Generative AI, and data analytics solutions across financial services, healthcare, retail, and enterprise domains. I have hands-on experience building end-to-end ML systems, including data ingestion, feature engineering, model training, deployment, monitoring, and continuous improvement in production.

I specialize in applying deep learning frameworks (TensorFlow, PyTorch, Keras) for classification, forecasting, NLP, and GenAI applications. I work with retrieval-augmented generation (RAG), semantic search, embeddings, and vector databases, leveraging LangChain and related tools. Proficient across AWS, Azure, and GCP, I implement scalable MLOps with MLflow, SageMaker, and container orchestration (Docker, Kubernetes), while collaborating with cross-functional teams to translate complex business needs into compliant, production-ready AI solutions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

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Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Senior AI/ML Engineer at Broadidge Financial Solutions

October 1, 2025 - Present

Collaborated with compliance and business teams to design AI-powered search capabilities that reduced manual regulatory research time by up to 40% across regulatory units. Engineered multi-agent AI workflows using LangChain and LangGraph to automate complex document analysis for regulatory and financial domain tasks. Implemented end-to-end ML pipelines including data ingestion, feature engineering, model training, deployment, monitoring, and continuous improvement. Optimized prompt templates and function-calling patterns to improve response accuracy and reduce hallucinations in LLM-powered applications. Managed vector databases and document embeddings with Azure OpenAI and Pinecone; integrated internal APIs with Generative AI services to enable context-aware user queries. Led deployment of fastAPI-based services on AKS, orchestrated by Docker and Kubernetes, and established monitoring with Azure Monitor and Application Insights; built CI/CD pipelines with GitHub Actions; implemented ML

AI/ML Engineer at Elevance Health

March 1, 2024 - October 1, 2025

Implemented end-to-end ML models for healthcare use cases including patient risk prediction, claims analytics, and care management optimization. Built scalable data pipelines with AWS, Spark, Airflow, and Azure Data Factory to process health care claims, provider, and member data. Engineered feature pipelines that transformed raw health data into meaningful features, improving model performance by 25% across patient risk and claims analytics. Developed NLP-based solutions to classify clinical documents and extract information from unstructured health records. Explored GenAI solutions using AWS Bedrock for clinical documentation summarization and care management workflows. Integrated MLflow-based workflows; deployed serverless inferences with AWS Lambda; monitored models with Azure Monitor; established governance for privacy and compliance.

ML Engineer / Data Scientist at Goldman Sachs

September 1, 2022 - February 1, 2024

Designed and deployed end-to-end ML models for fraud detection and transaction risk scoring using Python, scikit-learn, and XGBoost. Conducted exploratory data analysis on transaction datasets to identify fraud patterns and engineered features for risk scoring. Evaluated models with statistical techniques and cross-validation to ensure robustness across market conditions. Researched feature importance using SHAP and permutation importance to improve interpretability for compliance and risk teams. Integrated Kafka streams to consume real-time transaction events and feed downstream fraud detection models with up-to-date signals. Brought ML models into production via REST APIs using FastAPI, containerized with Docker and Kubernetes. Established comprehensive MLOps pipelines using MLflow for experiment tracking, versioning, and deployment; reduced training time by ~40% via scalable data processing and parallelization. Worked with risk and compliance teams to define model requirements and i

Data & ML Engineer / Retail Analytics at Walmart

March 1, 2019 - August 1, 2022

Collaborated with data science and business teams to prepare training datasets and engineering features used for demand forecasting, customer segmentation, and product recommendations using Azure Machine Learning. Built scalable data pipelines with Azure Data Factory to orchestrate data ingestion from enterprise sources into a centralized data lake for analytics and ML workloads. Developed Python-based preprocessing pipelines to standardize and clean large-scale retail datasets stored in ADLS Gen2 before consumption by ML models. Built feature-generation pipelines using PySpark and Azure Data Lake; aggregated customer purchase behavior, inventory metrics, and transactional data to support predictive analytics. Produced end-to-end ML pipelines on Azure ML to drive forecast accuracy and inventory planning. Deployed and monitored models on Azure ML and Kubernetes; collaborated with analysts and merchandising teams to deliver datasets and insights to improve promotional effectiveness and c

Junior Data Scientist at John Deere

March 1, 2017 - December 1, 2018

Supported development of ML models for IoT sensor telemetry from agricultural machinery using Python and scikit-learn. Processed large-scale manufacturing data using Pandas and SQL to surface equipment failure patterns and prepare clean training datasets. Wrote Python scripts for time-series forecasting to support inventory optimization, contributing to a 15% reduction in equipment downtime for agricultural machinery operations. Generated interactive data visualizations and daily performance dashboards using Tableau to meet senior stakeholder requirements. Transitioned local Python scripts and Jupyter notebooks into scheduled data pipelines under guidance of senior engineers, improving team workflow efficiency.