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I am an AI/ML engineer with 3 years of experience designing and deploying machine learning and deep learning solutions in the insurance and healthcare domains. I enjoy turning data into actionable insights and building production-ready ML systems. I have hands-on experience with Python, Scikit-learn, TensorFlow, PyTorch, NLP, LLMs (BERT, GPT-3.5), AWS/Azure, and MLOps, and I helped cut manual review times, scale processing to tens of thousands of records monthly, and implement real-time monitoring.…I am an AI/ML engineer with 3 years of experience designing and deploying machine learning and deep learning solutions in the insurance and healthcare domains. I enjoy turning data into actionable insights and building production-ready ML systems. I have hands-on experience with Python, Scikit-learn, TensorFlow, PyTorch, NLP, LLMs (BERT, GPT-3.5), AWS/Azure, and MLOps, and I helped cut manual review times, scale processing to tens of thousands of records monthly, and implement real-time monitoring.

Srikar Lakkimsetti

AI Engineer, AI Developer, AI Prompt Engineer, +6

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I am an AI/ML engineer with 3 years of experience designing and deploying machine learning and deep learning solutions in the insurance and healthcare domains. I enjoy turning data into actionable insights and building production-ready ML systems. I have hands-on experience with Python, Scikit-learn, TensorFlow, PyTorch, NLP, LLMs (BERT, GPT-3.5), AWS/Azure, and MLOps, and I helped cut manual review times, scale processing to tens of thousands of records monthly, and implement real-time monitoring.…I am an AI/ML engineer with 3 years of experience designing and deploying machine learning and deep learning solutions in the insurance and healthcare domains. I enjoy turning data into actionable insights and building production-ready ML systems. I have hands-on experience with Python, Scikit-learn, TensorFlow, PyTorch, NLP, LLMs (BERT, GPT-3.5), AWS/Azure, and MLOps, and I helped cut manual review times, scale processing to tens of thousands of records monthly, and implement real-time monitoring.

Available to hire

I am an AI/ML engineer with 3 years of experience designing and deploying machine learning and deep learning solutions in the insurance and healthcare domains. I enjoy turning data into actionable insights and building production-ready ML systems.

I have hands-on experience with Python, Scikit-learn, TensorFlow, PyTorch, NLP, LLMs (BERT, GPT-3.5), AWS/Azure, and MLOps, and I helped cut manual review times, scale processing to tens of thousands of records monthly, and implement real-time monitoring.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

Machine Learning Engineer at Allstate

August 1, 2024 - Present

Developed an anomaly detection pipeline using Isolation Forest and historical claims data to identify suspicious insurance activities, which reduced fraud analyst investigation time by 45% and improved detection precision across property and casualty segments. Engineered and deployed a credit risk prediction model using XGBoost and underwriting data, enhancing risk assessment accuracy by 32% and minimizing default rates in niche insurance markets. Built custom NLP pipelines to extract policy terms and regulatory clauses, accelerating claim review by 39%. Integrated Hugging Face transformer models with Airflow to auto-generate financial compliance summaries, reducing analyst workload by 65%. Designed A/B testing frameworks to evaluate predictive models, improving forecast accuracy by 21%. Streamlined feature engineering across actuarial datasets, reducing preprocessing time by 70%.

AI/ML Developer at Accenture

July 31, 2023 - July 22, 2025

Created a disease risk prediction model using XGBoost on structured EHR datasets, improving early diagnosis accuracy by 28%. Fine-tuned a domain-specific BERT model on over 80,000 patient reviews, increasing sentiment classification accuracy by 24%. Executed claims validation pipeline using OCR and CNNs, reducing manual insurance processing time by 47%. Deployed a GPT-3 powered summarization engine for physician-patient conversations, reducing clinical note-taking time by 38%. Configured containerized retraining pipelines on AWS EC2 with MLflow, achieving 99.8% uptime. Crafted interactive Tableau dashboards visualizing model outputs, enhancing decision-making. Performed hypothesis testing on treatment efficacy datasets, resulting in a 17% improvement in confidence scores.

Junior AI/ML Engineer at Accenture

July 31, 2022 - July 22, 2025

Architected deep learning models using PyTorch and TensorFlow to forecast stock price movements, reducing prediction error by 22%. Automated financial document classification workflows using CNNs and attention-based models, accelerating data extraction from over 60,000 invoices monthly and cutting compliance processing time by 58%. Devised churn prediction models using Logistic Regression, increasing retention rates by 19%. Established time-series forecasting pipelines with LSTM and Prophet, improving budget planning accuracy by 26%. Built real-time ML pipelines integrated with AWS for credit scoring, reducing loan default rates by 14%. Created KPI-driven Power BI dashboards to visualize model drift and risk clusters, enhancing executive visibility and audit transparency.

Machine Learning Engineer at Allstate

August 1, 2024 - Present

Developed an anomaly detection pipeline using Isolation Forest to reduce fraud analyst investigation time by 45%. Engineered a credit risk prediction model with XGBoost, improving risk assessment accuracy by 32%. Built custom NLP pipelines accelerating claim review by 39%. Integrated Hugging Face transformer models with Airflow for compliance summarization, reducing analyst workload by 65%. Designed A/B testing frameworks for policy lapse prediction improving forecast accuracy by 21%. Streamlined feature engineering to reduce preprocessing time by 70% and improved model stability for investment analysis.

AI/ML Developer at Accenture

July 31, 2023 - August 28, 2025

Created a disease risk prediction model using XGBoost that improved early diagnosis accuracy by 28%. Fine-tuned domain-specific BERT on patient reviews, increasing sentiment classification accuracy by 24%. Executed claims validation pipeline using OCR and TensorFlow CNNs reducing manual processing time by 47%. Deployed GPT-3 powered summarization engine for clinical notes reducing doctor note-taking time by 38%. Configured containerized retraining pipelines on AWS with MLflow for 99.8% uptime. Developed interactive Tableau dashboards for actionable clinical insights. Performed hypothesis testing to validate treatments, improving confidence scores by 17%.

Junior AI/ML Engineer at Accenture

July 31, 2022 - August 28, 2025

Architected deep learning models for stock price forecasting reducing prediction error by 22%. Automated financial document classification accelerating data extraction and cutting processing time by 58%. Developed churn prediction models boosting retention rates by 19%. Built time-series forecasting pipelines improving budget planning accuracy by 26%. Established real-time ML pipelines for credit scoring reducing loan defaults by 14%. Created KPI-driven Power BI dashboards enhancing executive visibility and audit transparency.

Machine Learning Engineer at Allstate

August 1, 2024 - Present

Designed ML pipelines using Python, Scikit-learn, and XGBoost to predict claim approval probability and fraud likelihood across multiple insurance products; developed TensorFlow models for claim severity estimation and premium adjustment; built PyTorch models with TorchServe for near real-time fraud detection and anomaly monitoring; implemented NLP pipelines with SpaCy and NLTK to extract insights from claim documents and feedback; engineered feature extraction workflows consolidating policy, claim, and customer data; applied LLMs (BERT, GPT-3.5) for policy summarization and entity tagging; deployed end-to-end ML workflows on Azure ML and Data Factory with automated retraining and drift monitoring; collaborated with engineering teams; created dashboards in Tableau to monitor KPIs.

AI/ML Developer at Accenture

July 1, 2023 - October 16, 2025

Constructed Python and Scikit-learn models for predicting patient readmission and risk stratification using structured EHR and unstructured clinical notes; engineered feature extraction pipelines; improved manual chart review time from 3 hours to 50 minutes; trained deep learning models for radiology image classification; established PyTorch models for ICU deterioration prediction with real-time streaming; designed NLP pipelines to extract insights from clinical notes; deployed ML pipelines on AWS SageMaker and EC2 with automated retraining and monitoring; built Tableau dashboards with 50+ KPIs; deployed BERT and GPT-based NLP models for clinical report summarization and decision support.