Mohammed Husaain

AI Solutions Engineer with 7+ years of experience designing, deploying, and supporting enterprise cloud infrastructure and production AI integrations. Specializes in bridging the gap between core engineering platforms and live enterprise environments by integrating generative AI models, building custom RAG pipelines, and deploying low-latency API gateways directly into existing business workflows. Hands-on experience deploying machine learning workflows and generative AI architectures into enterprise data ecosystems.

Proficient in Python, Go, and SQL to extract, validate, and orchestrate data flows across hybrid cloud and containerized environments. Experienced in building automated data ingestion pipelines, configuring vector search indexes for context retrieval, and integrating streaming gRPC/REST APIs to connect agentic workflows with backend systems. Proven track record operating as a primary technical escalation point for complex infrastructure bottlenecks, API rate limits, and model inference latency.

Skilled in troubleshooting container workloads on Kubernetes, automating CI/CD and MLflow delivery pipelines, and enforcing strict compliance standards across healthcare, energy, and financial environments.

AI Solutions Engineer at Humana (2025-11 – Present)

• Deployed enterprise Gemini models on Vertex AI to support agent workflows for real-time customer care operations.
• Built custom RAG pipelines using LangChain and Vertex AI Vector Search to retrieve internal clinical policy documents accurately.
• Implemented Python middleware and GCP VPC Service Controls to scrub Protected Health Information before passing prompts to LLM endpoints.
• Configured Dialogflow CCAI with streaming gRPC endpoints to transcribe customer audio and trigger contextual recommendations for agents.
• Deployed microservices on Google Kubernetes Engine running Container-Optimized OS to host backend API services handling high-volume claims queries.
• Automated prompt deployment and model validation pipelines using Cloud Build and GitHub Actions across staging and production environments.
• Monitored Gemini API context window quotas, token throughput, and rate limits to prevent query drops during peak call windows.
• Developed automated PyTest suites to evaluate LLM responses for factual accuracy, hallucinations, and clinical policy alignment before release.
• Configured Cloud Armor and API Gateway policies to protect generative endpoints from unauthorized access and malicious payload injection.
• Optimized vector retrieval latency by caching frequent embeddings in Redis and tuning similarity search parameters in Vertex AI.
• Integrated Cloud Logging and error tracking dashboards to detect prompt drift, response degradation, and upstream API connection timeouts.
• Developed TypeScript client SDKs to connect the agent-assist web portal with underlying LLM streaming backends efficiently.
• Integrated LlamaIndex to structure complex medical plan documentation into hierarchical nodes, reducing irrelevant chunk retrieval during inference.
• Debugged token exhaustion errors and memory leaks across GKE inference pods to maintain continuous 24/7 service availability.
• Documented AI integration blueprints, prompt versioning runbooks, and HIPAA compliance verification procedures for engineering and operations teams.

Cloud Solutions Engineer at ERCOT (2025-01 – 2025-10)

• Built multi-cloud data infrastructure on Azure to ingest real-time electrical grid telemetry for predictive load forecasting models.
• Automated deployment of Azure Machine Learning workspaces and compute clusters using reusable Terraform modules across multiple environments.
• Configured Databricks runtime clusters to process streaming SCADA sensor data with Python and Pandas for downstream model ingestion.
• Integrated MLflow with Azure DevOps pipelines to track model experiment metrics, register artifacts, and automate staging promotion gates.
• Configured Azure ExpressRoute circuits and secure private endpoints to isolate high-throughput grid telemetry streams from public internet traffic.
• Monitored telemetry ingestion pipeline latency and compute cluster resource utilization using Splunk alerts and Azure Monitor metrics.
• Developed automated PyTest validation suites to verify schema integrity and prevent corrupt time-series data from entering ML pipelines.
• Tuned Scikit-learn and time-series model inference jobs to reduce regional electricity demand calculation times during peak usage.
• Patched and maintained Red Hat Enterprise Linux and Windows Server instances supporting telemetry ingestion systems to ensure reliability.
• Optimized complex SQL queries on relational data stores to extract historical energy consumption records for model backtesting.
• Built GitHub Actions workflows to automate unit testing, linting, and deployment of data ingestion scripts into Azure VMs.
• Debugged pipeline bottlenecks and network timeouts during extreme weather grid fluctuations, restoring real-time data flow without data loss.
• Documented data pipeline schemas, failover procedures, and infrastructure topology to satisfy regional electrical grid reliability compliance audits.

Cloud Engineer at Walmart (2023-09 – 2025-01)

• Built multi-cloud compute environments across Azure and OpenStack to run Element ML training and large-scale search workloads reliably.
• Containerized PyTorch and TensorFlow model training jobs onto Walmart Cloud Native Platform Kubernetes clusters to scale distributed compute.
• Automated ML model artifact packaging and continuous deployment pipelines using Looper and Concord across internal testing and production environments.
• Developed custom Go microservices to automate dynamic resource allocation and prevent GPU compute starvation during peak training cycles.
• Configured Istio service mesh routing policies and mutual TLS to secure high-throughput east-west microservice communication across clusters.
• Integrated vector database indexing clusters into search backend services to support semantic search feature retrieval at high concurrency.
• Implemented automated end-to-end integration tests using TestArmada to validate model serving API latency under simulated production traffic.
• Upgraded GPU-enabled CentOS virtual machine templates in OpenStack to support newer deep learning drivers and CUDA libraries.
• Monitored container resource consumption and network ingress throughput to detect bottlenecked nodes before high-volume retail sales events.
• Debugged cross-cloud networking packet drops and ingress routing failures, restoring seamless traffic failover between Azure and private data centers.
• Documented platform integration standards and technical RFCs to guide engineering teams in onboarding ML workloads to WCNP clusters.

DevOps/Automation Engineer at Bank of America (2023-01 – 2023-09)

• Automated deployment pipelines for hosting proprietary financial models across secured Red Hat OpenShift clusters to streamline enterprise software releases.
• Built reusable Ansible playbooks in Ansible Tower to automate security hardening and configuration updates across RHEL 8 server fleets.
• Configured XL Release pipelines integrated with Bitbucket to enforce automated change management approvals and strict banking compliance controls.
• Implemented SonarQube code quality gates within Jenkins CI pipelines to block builds violating corporate security and vulnerability standards.
• Configured F5 Big-IP load balancing pools and health checks to maintain high availability for internal financial automation portals.
• Developed Python automation scripts to extract, validate, and process batch analytics data between legacy hosts and OpenShift containers.
• Standardized base Docker container images for internal analytics services, reducing common package vulnerabilities across development and production environments.
• Troubleshot and resolved OpenShift pod eviction issues and persistent storage mount failures during scheduled high-concurrency batch execution windows.

System Administrator at Talent Cogent Inc (2020-02 – 2021-03)

• Administered bare-metal and VMware ESXi virtualization infrastructure across enterprise client environments to ensure high server uptime and reliability.
• Automated routine system patching and user provisioning across Windows Server 2016 Active Directory domains using PowerShell scripts.
• Migrated legacy on-premise physical workloads to Hyper-V virtual environments to consolidate hardware footprint and improve server management.
• Configured enterprise IPsec VPN gateways and managed DNS, DHCP, and VLAN network routing for remote workforce connectivity.
• Implemented automated SQL database and server backup routines to external storage devices to prevent mission-critical data loss.
• Deployed basic Jenkins automation jobs and Bash scripts to streamline scheduled build artifacts and software patch deployments.
• Documented standard operating procedures, disaster recovery runbooks, and hardware inventory logs to maintain infrastructure.

Masters in Computer Science from Governors State University, 2022