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VENKAT - Forward Deployed Engineer

narendra@symantrix.xom

Location: Apex, North Carolina, USA

Relocation: no

Visa: GREEN CARD

Resume file: Venkat_Alluri_FDE_Resume_1789478108960.docx Please check the file(s) for viruses. Files are checked manually and then made available for download.

(469) 817-9990 VENKAT RAMA RAJU ALLURI PRINCIPAL FORWARD DEPLOYED ENGINEER | AI/ML APPLICATIONS, AGENTS & PLATFORMS Apex, NC | linkedin.com/in/venkatramarajualluri | U.S. Permanent Resident PROFESSIONAL SUMMARY Principal-level Forward Deployed Engineer and AI/ML platform leader with 15+ years translating ambiguous customer and engineering needs into production applications, machine learning systems, generative AI agents, developer platforms, and cloud solutions. Hands-on across discovery, solution architecture, model and application development, deployment, governance, monitoring, adoption, and production operations. Deep experience with Python, Databricks, AWS, Kubernetes, Terraform, Ansible configuration management, and Crossplane-based infrastructure delivery through Argo CD GitOps. CORE COMPETENCIES Forward Deployed Engineering: Customer discovery, requirements analysis, solution design, rapid prototyping, implementation, integration, production rollout, enablement, adoption, and operational handoff AI/ML and Application Engineering: Traditional machine learning, GenAI agents, Python, Streamlit, Databricks Apps, Genie, MLflow, SageMaker, model serving, APIs, evaluation, governance, and monitoring Cloud and Production Engineering: AWS, Databricks, Kubernetes/EKS, Terraform, Ansible configuration management, Crossplane, Argo CD GitOps, CI/CD, observability, SRE, security, performance, incident response, and disaster recovery PROFESSIONAL EXPERIENCE Staff MLOps Engineer - AI Infrastructure and Platform Engineering | Western Governors University (WGU) Raleigh, NC | February 2026 - Present Embedded with data science, engineering, product, security, and governance teams to design, build, deploy, and operate traditional machine-learning solutions and generative AI applications on the Databricks platform. Led customer and stakeholder discovery to define business use cases, user workflows, data requirements, model objectives, nonfunctional requirements, security controls, evaluation criteria, and measurable production outcomes. Designed and developed traditional machine-learning models in Python, including data preparation, feature engineering, experimentation, training, validation, inference, and integration into enterprise workflows. Built generative AI agents and agentic applications using Databricks-native capabilities, Genie Spaces, Genie Agents, Databricks Apps, Genie One, and Genie Anatomy, connecting governed enterprise data with natural-language and tool-enabled user experiences. Developed Python services, reusable agent patterns, application components, APIs, workspace templates, and integration layers that converted proofs of concept into supportable production solutions. Implemented model and agent evaluation practices covering task accuracy, response quality, groundedness, relevance, safety, latency, reliability, and regression testing before production promotion. Established MLflow-based experiment tracking, model and artifact versioning, lineage, model registration, tracing, evaluation results, approval workflows, and reproducible development across environments. Deployed traditional models, GenAI agents, and Databricks applications through automated CI/CD workflows with testing, versioning, environment promotion, release approvals, rollback controls, and production readiness gates. Applied enterprise AI governance through Unity Catalog, role-based access control, service principals, secrets management, data permissions, lineage, auditability, policy enforcement, and separation of development, staging, and production. Implemented production monitoring using MLflow tracing, Databricks telemetry, OpenTelemetry, Prometheus, Grafana, and CloudWatch to track model and agent quality, application errors, latency, token and infrastructure cost, availability, and operational health. Led architecture reviews, stakeholder demonstrations, user acceptance, security reviews, launch planning, incident response, feedback loops, documentation, knowledge transfer, and post-launch adoption; mentored engineers and standardized delivery patterns. Technologies: Python, Databricks, Genie Spaces, Genie Agents, Databricks Apps, Genie One, Genie Anatomy, MLflow, Unity Catalog, AWS, Kubernetes, Terraform, GitHub Actions, ArgoCD, OpenTelemetry, Prometheus, Grafana, CloudWatch Lead Principal Platform and DevOps Engineer | Dizer Corp - Client: J.Crew Remote, USA | October 2022 - February 2026 Served as the principal hands-on technical lead embedded with a major retail customer, owning discovery, architecture, application delivery, platform modernization, and production outcomes for e-commerce and developer-experience solutions. Partnered with product, application, SRE, security, QA, and operations teams to identify high-value use cases, establish success measures, and translate production constraints into target architectures and phased delivery plans. Designed and developed Python-based models and decision services for internal DevOps and Internal Developer Platform tools, including deployment-risk scoring, operational analysis, release validation, health assessment, and automated recommendations. Built Streamlit applications that delivered platform insights and self-service developer workflows by integrating Python services with CI/CD systems, Kubernetes, AWS services, observability data, and enterprise APIs. Owned design-to-production execution by defining application architecture, developing Python services, containerizing workloads, implementing automated tests, building release pipelines, and deploying applications to Amazon EKS through GitOps. Architected multi-region Amazon EKS infrastructure and reusable Terraform, Crossplane, Ansible, Helm, and Argo CD components supporting 50+ microservices with automated scaling, failover, self-healing, and zero-downtime releases. Implemented Crossplane as a Kubernetes-native Infrastructure as Code option for selected AWS resources, defining reusable compositions and managing infrastructure changes through Git pull requests, policy reviews, and Argo CD reconciliation alongside Terraform-managed infrastructure. Used Ansible for configuration management, system hardening, middleware and agent installation, environment-specific configuration, patching, and repeatable operational changes across cloud and platform environments. Implemented CI/CD and release strategies using GitHub Actions, Jenkins, ArgoCD, Helm, NGINX, Kong, AWS load balancers, service discovery, blue/green releases, canary deployments, and rollback controls, reducing deployment failures by 40%. Instrumented Streamlit applications, Python services, Kubernetes workloads, and delivery pipelines with OpenTelemetry, Prometheus, Grafana, CloudWatch, and Splunk, reducing mean time to recovery by 50%. Built governed ML lifecycle capabilities with Amazon SageMaker Studio, Pipelines, Model Registry, MLflow, Endpoints, Feature Store, and Model Monitor for multi-tenant experimentation, training, approvals, real-time and batch inference, drift detection, and production promotion. Led architecture and production-readiness reviews, stakeholder demonstrations, rollout planning, incident response, product feedback, code reviews, mentoring, documentation, enablement, adoption, and operational handoff across distributed teams. Technologies: AWS, Amazon EKS, Python, Streamlit, SageMaker, MLflow, Kubernetes, Terraform, Crossplane, Ansible, GitHub Actions, Jenkins, ArgoCD, Helm, NGINX, Kong, OpenTelemetry, Prometheus, Grafana, CloudWatch, Splunk Platform Engineering Expert | Novartis Healthcare Remote, USA | June 2020 - October 2022 Delivered secure cloud and developer-platform solutions for global research and engineering organizations operating in regulated enterprise environments. Worked with engineering, infrastructure, security, and compliance stakeholders to translate cloud adoption needs into scalable multi-account AWS platform architecture, delivery roadmaps, and governance controls. Built reusable Terraform modules and Ansible configuration-management roles that reduced infrastructure provisioning and configuration time by more than 80% while standardizing system hardening, package installation, application configuration, patching, and repeatable operational changes. Implemented CI/CD, Kubernetes platform capabilities, and deployment automation that improved delivery speed, reliability, and repeatability across hundreds of workloads. Designed IAM, encryption, secrets management, backup, disaster recovery, compliance controls, monitoring, and operational dashboards for secure and supportable production environments. Modernized legacy application infrastructure, resolved platform reliability issues, contributed to architecture reviews, and mentored engineers on cloud-native engineering and operational excellence. EARLIER EXPERIENCE Senior Associate | DBS Asia Hub 2 | Hyderabad, India | 2019 - 2020 Senior Software Engineer | Oracle | Hyderabad, India | 2017 - 2019 Software Engineer - MTS | Salesforce | Hyderabad, India | 2016 - 2017 Assistant Manager - Technology | IPay | Hyderabad, India | 2013 - 2016 Software Engineer | CSC Ltd. (DXC Technology) | Hyderabad, India | 2010 - 2013 TECHNICAL SKILLS Programming and Application Development: Python, Java, Bash, Streamlit, REST APIs, microservices, distributed applications Machine Learning and Generative AI: Traditional ML, generative AI agents, agentic applications, Databricks, Genie Spaces, Genie Agents, Databricks Apps, Genie One, Genie Anatomy, SageMaker, MLflow, Kubeflow, PyTorch, TensorFlow Cloud, Configuration and Platform Engineering: AWS, Kubernetes, Amazon EKS, Docker, Helm, Argo CD, GitOps, Terraform, Crossplane compositions and providers, Ansible roles and playbooks, configuration management, AWS CDK, Jenkins, GitHub Actions MLOps, Evaluation and Governance: Experiment tracking, feature engineering, model training, model registry, model serving, agent evaluation, tracing, lineage, Unity Catalog, RBAC, CI/CD, drift and data-quality monitoring Observability, Reliability and Security: OpenTelemetry, Prometheus, Grafana, CloudWatch, Splunk, SLI/SLO, incident response, IAM, secrets management, disaster recovery CERTIFICATIONS AWS Certified Solutions Architect - Professional | AWS Certified DevOps Engineer - Professional Certified Kubernetes Administrator (CKA) | Certified Kubernetes Application Developer (CKAD) Argo CD Fundamentals | Kong Gateway Foundations | MLOps with Databricks | MLflow and Hugging Face | Governing AI Agents EDUCATION Bachelor of Technology (B.Tech.), Computer Science and Engineering | Jawaharlal Nehru Technological University, Hyderabad | 2006 - 2010 Keywords: continuous integration continuous deployment quality analyst artificial intelligence machine learning North Carolina