Naveera Technology LLC

Engineering Manager

• Lead large-scale AWS-to-GCP data platform migration strategy and execution
• Design and implement enterprise GCP data platforms, Data Lake and Lakehouse architectures, and Bronze, Silver, and Gold/Atomic data layers
• Design and implement AI/ML and Generative AI solutions on GCP using Vertex AI and related GCP-native services
• Build production-grade machine learning pipelines for preparation, training, validation, evaluation, deployment, monitoring, retraining, and lifecycle management
• Develop GenAI and RAG solutions including enterprise search, document intelligence, AI assistants, summarization, semantic search, embeddings, vector search, and knowledge-management applications
• Implement MLOps practices, model versioning, experiment tracking, validation, automated testing, deployment approvals, rollback, and environment promotion
• Establish monitoring for model performance, data drift, latency, reliability, inference cost, response quality, retrieval accuracy, hallucination, and prompt injection risks
• Ensure responsible AI, data privacy, security, governance, access control, auditability, and human-review processes
• Analyze AWS platforms and map S3, Glue, Redshift, Athena, Step Functions, DMS, and Lake Formation workloads to GCP services
• Architect real-time pipelines using Pub/Sub, Dataflow/Apache Beam, BigQuery, and Cloud Storage
• Design and implement batch and streaming ETL/ELT, CDC, transformation, orchestration, and data processing pipelines using Python, PySpark, SQL, dbt, BigQuery, Cloud Composer/Airflow, and Dataflow
• Define migration phases, technical dependencies, risks, rollback strategies, target-state architecture, and modernization opportunities
• Design enterprise data models, dimensional/normalized/denormalized structures, multi-tenant data models, BigQuery partitioning and clustering strategies
• Establish data governance, data quality, lineage, metadata, ownership, IAM, encryption, service accounts, network security, and data access policies
• Lead Terraform-based infrastructure automation, CI/CD, testing, deployment, and provisioning across Dev, QA, UAT, and Production
• Optimize BigQuery, Dataflow, Spark, Cloud Storage, streaming workloads, performance, SLAs, and cloud costs
• Lead and mentor Data Engineers, Senior Data Engineers, and Technical Leads; provide technical direction, conduct architecture and code reviews, and define roadmaps
• Act as primary technical contact for US-based stakeholders and collaborate with Business, Product, Data Science, BI, DevOps, Security, and Analytics teams
• Translate business requirements into scalable technical solutions and communicate architecture decisions, risks, dependencies, timelines, trade-offs, and KPIs

Head of Engineering – GCP AI/ML, GenAI

QA Automation Engineer

• Lead the architecture and development of AI-driven automation platforms and intelligent workflows
• Build scalable Python backend services, REST APIs, asynchronous processing, and microservices
• Design workflow orchestration using Temporal or equivalent workflow engines for long-running business processes
• Develop AI agents using modern LLM frameworks and AWS AI services
• Integrate Python services with C#/.NET applications, enterprise systems, and external APIs
• Design secure, scalable, stateful/stateless backend architectures with event-driven patterns
• Implement authentication and authorization using OAuth2, JWT, SSO, RBAC/ABAC
• Develop AI-powered healthcare workflows across RCM, enrollment, claims, payer/provider integrations, and document automation
• Build full-stack AI applications spanning backend APIs, workflow services, AI orchestration, and frontend integration
• Implement AI Ops practices including monitoring, observability, model lifecycle management, logging, evaluation, and production support
• Drive engineering best practices, code quality, CI/CD, and documentation
• Mentor development teams and collaborate with cross-functional teams

Senior/Lead Data Scientist – AIML Engineer, AI Automation Engineer

Frontend Engineer

• Own large technical initiatives from design through delivery
• Make architectural decisions and modernize legacy applications
• Solve complex business problems end-to-end
• Design, optimize, and support scalable service-based or distributed systems
• Build or maintain high-volume batch processing and background job systems
• Process large datasets related to eligibility, claims, and remittance files
• Translate regulated insurance and payments workflows into scalable technical solutions
• Partner with Product and domain experts to solve ambiguous problems
• Mentor other engineers and raise the team’s technical bar
• Consider long-term business and technical ROI when building solutions
• Work across the Microsoft stack, including .NET Core/.NET Framework and SQL Server
• Develop modern front-end solutions with React and Angular
• Work in AWS environments using CI/CD practices
• Leverage AI-assisted development tools to improve development velocity and software quality

Head of Engineering – GCP AI/ML, GenAI

Senior/Lead Data Scientist – AIML Engineer, AI Automation Engineer

Senior .NetCore Engineer, ReactJS, Claude AI