AI/ML Engineer with experience designing, developing, and deploying intelligent software solutions using machine learning, deep learning, and Large Language Models (LLMs). Skilled in building NLP applications, RAG pipeli…
AI/ML Engineer with experience designing, developing, and deploying intelligent software solutions using machine learning, deep learning, and Large Language Models (LLMs). Skilled in building NLP applications, RAG pipelines, model fine-tuning, prompt engineering, and scalable AI workflows using Python and modern ML frameworks. Experienced in translating business problems into production-ready AI systems, integrating APIs, vector databases, and cloud technologies to deliver measurable impact. Passionate about building practical, user-focused AI products that improve efficiency and decision-making.
Orchestrated agent-based architectures in backend systems engineering to facilitate agentic workflows, resulting in improved system observability and a 99.95% reliability rate across distributed services during peak usage.
Engineered large language model (LLM) powered services within production backend distributed systems, enabling scalable AI features that optimized performance and reduced latency across multiple microservices, resulting in a 30% increase in throughput.
Orchestrated HIPAA-compliant ingestion pipelines in AWS S3, Glue, and Airflow, delivering curated clinical features to SageMaker training jobs and dependable patient-risk predictions at scale.
Deployed containerized inference services with Docker and Kubernetes, integrating REST APIs and API Gateway, ensuring low-latency scoring for care-management applications and audits securely today.
Validated model performance with A/B testing, MLflow tracking, and bias checks, improving governance readiness and enabling clinicians to trust model recommendations during triage consistently.
Instrumented end-to-end monitoring with CloudWatch, Prometheus, and alerting runbooks, reducing production incidents, accelerating recovery, and safeguarding SLA commitments for critical analytics consumers every release.
Standardized feature store practices with Spark, Parquet, and SQL, minimizing data drift, improving lineage, and accelerating reproducible experimentation for new cohort-stratification models across teams.
Orchestrated agent-based architectures in backend systems engineering, Engineered large language model (LLM) powered services, Orchestrated HIPAA-compliant ingestion pipelines, Deployed containerized inference services, Validated model performance with A/B testing, Instrumented end-to-end monitoring, Standardized feature store practices
Spearheaded deployment challenges by creating evaluation frameworks that streamlined cost management processes, delivering a tangible impact through a 25% reduction in operational expenses and improving deployment success rates significantly.
Engineered fraud-detection pipelines with Kafka, Spark Streaming, and Python, transforming event data into real-time features and reliable anomaly alerts for investigators daily at scale.
Modernized MLOps workflows with GitHub Actions, Terraform, and Kubernetes, enabling repeatable deployments, separation-of-duties controls, auditable rollbacks, consistent promotion, and faster approvals across environments securely.
Analyzed model explainability with SHAP, governance documentation, and lineage tooling, supporting model risk reviews and improving transparency for compliance stakeholders and business partners proactively.
Secured data access with IAM, KMS, and VPC endpoints, protecting sensitive banking datasets and enabling controlled experimentation without operational interruptions across teams enterprise-wide continuously.
Optimized batch scoring jobs on AWS SageMaker and EMR, reducing compute waste, improving job reliability, and delivering timely portfolio insights to downstream reporting systems.
Spearheaded deployment challenges by creating evaluation frameworks, Engineered fraud-detection pipelines, Modernized MLOps workflows, Analyzed model explainability, Secured data access, Optimized batch scoring jobs
Modernized AI systems using modern systems programming languages to achieve substantial latency reduction, enhancing user experience through optimized algorithms that processed requests 40% faster in high-load scenarios.
Integrated claims and eligibility datasets with Snowflake, dbt, and SQL, producing trusted analytics marts that accelerated risk-adjustment model development and validation across programs nationwide.
Automated retraining triggers with Airflow, Feature Store, and model registry, keeping deployed models current, reducing drift, and improving consistency across member populations reliably monthly.
Refactored NLP pipelines with Transformers and PyTorch, extracting clinical concepts from notes and strengthening downstream decision-support workflows for care teams daily at scale enterprise-wide.
Configured retrieval-augmented generation with AWS Bedrock, LangChain, and OpenSearch, enabling secure question answering over policy content and operational procedures for agents securely, compliant, scalable.
Governed experiment tracking with MLflow, DVC, and reproducible notebooks, improving collaboration, auditability, and handoffs between data science and engineering squads across releases end-to-end consistently.
Modernized AI systems using modern systems programming languages, Integrated claims and eligibility datasets, Automated retraining triggers, Refactored NLP pipelines, Configured retrieval-augmented generation, Governed experiment tracking
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