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I am a seasoned AI engineer with over 5 years of experience building and deploying production AI/ML and Generative AI solutions across cloud and on-prem environments. I design and implement LLM-based applications, Retrieval-Augmented Generation (RAG), vector search, and agentic tool-use workflows. I’m proficient in Python, PySpark, SQL, Spark/Databricks, and crafting end-to-end pipelines from data preparation to training, evaluation, and low-latency serving. I’ve led secure, compliant deployments with IAM/VPC controls, OAuth2/RBAC, observability, and SLO/SLI practices, and I enjoy collaborating across teams in Agile settings to drive measurable business outcomes. I thrive in cross-functional, fast-paced environments, mentoring teammates, and conducting A/B and multivariate tests to optimize performance. I’ve built production-grade MLOps with MLflow, containerization (Docker), Kubernetes/EKS, and Terraform, and I’ve delivered scalable inference services and RAG stores across AWS, GCP, and on-prem. My work focuses on turning complex data into practical, impactful AI-powered products while maintaining security, governance, and operational excellence.…I am a seasoned AI engineer with over 5 years of experience building and deploying production AI/ML and Generative AI solutions across cloud and on-prem environments. I design and implement LLM-based applications, Retrieval-Augmented Generation (RAG), vector search, and agentic tool-use workflows. I’m proficient in Python, PySpark, SQL, Spark/Databricks, and crafting end-to-end pipelines from data preparation to training, evaluation, and low-latency serving. I’ve led secure, compliant deployments with IAM/VPC controls, OAuth2/RBAC, observability, and SLO/SLI practices, and I enjoy collaborating across teams in Agile settings to drive measurable business outcomes. I thrive in cross-functional, fast-paced environments, mentoring teammates, and conducting A/B and multivariate tests to optimize performance. I’ve built production-grade MLOps with MLflow, containerization (Docker), Kubernetes/EKS, and Terraform, and I’ve delivered scalable inference services and RAG stores across AWS, GCP, and on-prem. My work focuses on turning complex data into practical, impactful AI-powered products while maintaining security, governance, and operational excellence.

Mohammad Shoaib

AI Engineer, AI Developer, Data Scientist, +2

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I am a seasoned AI engineer with over 5 years of experience building and deploying production AI/ML and Generative AI solutions across cloud and on-prem environments. I design and implement LLM-based applications, Retrieval-Augmented Generation (RAG), vector search, and agentic tool-use workflows. I’m proficient in Python, PySpark, SQL, Spark/Databricks, and crafting end-to-end pipelines from data preparation to training, evaluation, and low-latency serving. I’ve led secure, compliant deployments with IAM/VPC controls, OAuth2/RBAC, observability, and SLO/SLI practices, and I enjoy collaborating across teams in Agile settings to drive measurable business outcomes. I thrive in cross-functional, fast-paced environments, mentoring teammates, and conducting A/B and multivariate tests to optimize performance. I’ve built production-grade MLOps with MLflow, containerization (Docker), Kubernetes/EKS, and Terraform, and I’ve delivered scalable inference services and RAG stores across AWS, GCP, and on-prem. My work focuses on turning complex data into practical, impactful AI-powered products while maintaining security, governance, and operational excellence.…I am a seasoned AI engineer with over 5 years of experience building and deploying production AI/ML and Generative AI solutions across cloud and on-prem environments. I design and implement LLM-based applications, Retrieval-Augmented Generation (RAG), vector search, and agentic tool-use workflows. I’m proficient in Python, PySpark, SQL, Spark/Databricks, and crafting end-to-end pipelines from data preparation to training, evaluation, and low-latency serving. I’ve led secure, compliant deployments with IAM/VPC controls, OAuth2/RBAC, observability, and SLO/SLI practices, and I enjoy collaborating across teams in Agile settings to drive measurable business outcomes. I thrive in cross-functional, fast-paced environments, mentoring teammates, and conducting A/B and multivariate tests to optimize performance. I’ve built production-grade MLOps with MLflow, containerization (Docker), Kubernetes/EKS, and Terraform, and I’ve delivered scalable inference services and RAG stores across AWS, GCP, and on-prem. My work focuses on turning complex data into practical, impactful AI-powered products while maintaining security, governance, and operational excellence.

Available to hire

I am a seasoned AI engineer with over 5 years of experience building and deploying production AI/ML and Generative AI solutions across cloud and on-prem environments. I design and implement LLM-based applications, Retrieval-Augmented Generation (RAG), vector search, and agentic tool-use workflows. I’m proficient in Python, PySpark, SQL, Spark/Databricks, and crafting end-to-end pipelines from data preparation to training, evaluation, and low-latency serving. I’ve led secure, compliant deployments with IAM/VPC controls, OAuth2/RBAC, observability, and SLO/SLI practices, and I enjoy collaborating across teams in Agile settings to drive measurable business outcomes.

I thrive in cross-functional, fast-paced environments, mentoring teammates, and conducting A/B and multivariate tests to optimize performance. I’ve built production-grade MLOps with MLflow, containerization (Docker), Kubernetes/EKS, and Terraform, and I’ve delivered scalable inference services and RAG stores across AWS, GCP, and on-prem. My work focuses on turning complex data into practical, impactful AI-powered products while maintaining security, governance, and operational excellence.

Skills

Experience Level

Expert

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Expert

Expert

Work Experience

AI/ML Engineer (Generative AI, RAG, MLOps) at Cortracker

May 1, 2024 - Present

Designed and deployed Generative AI solutions for content generation, summarization, and conversational AI using GPT-3/4 and BERT-based NLP components. Implemented Retrieval-Augmented Generation (RAG) and agentic workflows with tool use, slot filling, and safe schema-controlled completions. Built end-to-end pipelines for data processing, feature generation, and low-latency inference. Established real-time decisioning service on Kubernetes/EKS with OAuth2/RBAC, batching, and GPU-aware autoscaling. Implemented vector search (FAISS, Pinecone) and multimodal embeddings; built OCR/NER extraction and enterprise document workflows. Set up CI/CD with GitHub Actions/Jenkins, containerization with Docker, and Terraform for infra; improved observability with OpenTelemetry and Grafana; defined SLOs/SLIs and benchmarking.

AI/ML Engineer at Moody’s

January 1, 2023 - May 1, 2024

Built scalable cloud-native Python microservices (FastAPI) on Kubernetes/EKS integrating SageMaker endpoints and Snowflake features for production inference. Implemented drift monitoring and experiment readouts, maintained a lightweight feature-store, and supported A/B and multivariate tests for ranking features. Established observability with Prometheus, Grafana, CloudWatch; orchestrated ETL with Airflow; refreshed embeddings for RAG stores and search systems. Enforced governance with RBAC, runbooks, and SLO/SLA documentation; supported secure data modernization using Databricks and Azure services.

AI/ML Engineer at Mayo Clinic

January 1, 2021 - July 1, 2022

Developed TensorFlow/PyTorch models for image classification, object detection/tracking, and NLP tasks in healthcare; enhanced enterprise search using Elasticsearch/OpenSearch and semantic ranking. Deployed low-latency model services on Kubernetes; tuned throughput/latency budgets with tracing and monitoring. Containerized models as microservices; delivered scalable production APIs. Implemented MLOps practices including model versioning, drift detection, automated retraining, and SageMaker-based pipelines for continuous training and serving.

Data Scientist / Analyst at Symplore Inc

February 1, 2020 - January 1, 2021

Built predictive models for segmentation, churn, and recommendation using Python and scikit-learn; deployed big-data pipelines with Apache Spark and Hadoop. Automated ETL with Apache NiFi and Talend; deployed models via Docker and Jenkins. Managed Kubernetes assets and autoscaled GPU jobs; built stakeholder dashboards in Grafana/Tableau/Power BI; performed EDA and statistical analyses to support data-driven decisions.