Data Engineering Manager - (GCP, AI/ML & GenAI) @ Naveera Tech, USA - Remote Work

Greetings of the day!!

I am Arumugam Veera, reaching out to you regarding an exciting career opportunity with Naveera Technology LLC. I would be happy to connect and discuss the opportunity further. You can also connect with me on LinkedIn:https://www.linkedin.com/in/arumugamv/

About Naveera Technology LLC

Naveera Technology LLC is a trusted global engineering partner delivering Data Engineering, Generative AI, Application Development, and IT Infrastructure solutions. With over 15 years of experience in IT services and consulting, we help organizations transform raw data into actionable business value.

With a team of 100+ employees and successful delivery of 3+ global projects, Naveera serves clients across multiple industries and geographies through agile delivery models and proven engineering practices.

From Digital Health and Financial Services to E-Commerce and Technology, we support a diverse client base and back every engagement with proven frameworks, low-attrition teams, and scalable global delivery capabilities. At Naveera, we empower organizations to turn challenges into opportunities, data into insights, and innovative ideas into enterprise-grade platforms.

Specialties
Data Engineering & Modern Data Stack, Generative AI Solutions & Model Deployment, Application Development (Web, Mobile & Enterprise), Artificial Intelligence (Predictive, Conversational, Computer Vision), IT Infrastructure Services (Cloud & On-Prem), Security, DR, Cloud Transformation & Microservices, DevOps, API & Systems Integration, Extended Technology Teams & Dedicated Delivery, Real-Time Streaming & Analytics, and BI & Data Warehousing

Job Title: Data Engineering Manager - (GCP, AI/ML & GenAI)

Experience: 15+ Years

Location: Remote (USA)

Primary Focus: GCP Data Engineering & AWS, AI/ML & GenAI

Note: Preferrably we are looking for hands on experience as a Head of Engineering/ Engineering manager in GCP Platform and if you are currently working as a Senior/Lead Data Engineer then your profile is not suitable for the current requirement.

Position Overview

We are looking for an experienced Engineering Manager with strong hands-on expertise in AWS and GCP Data Engineering to lead a large-scale AWS-to-GCP data platform migration.

The ideal candidate will have strong experience designing enterprise data platforms on AWS and migrating them to Google Cloud Platform (GCP). The role requires a combination of technical architecture, hands-on engineering, migration leadership, team management and stakeholder management.

The candidate should have strong hands-on experience with AWS services such as S3, Glue, Redshift, Athena, Step Functions and AWS DMS, along with strong GCP expertise across BigQuery, Dataflow, Pub/Sub, Cloud Storage and Cloud Composer.

The Engineering Manager will work closely with US-based stakeholders, architects, engineers, DevOps teams, Data Science and BI teams to define the migration strategy and ensure successful execution.

Key Responsibilities

1. AI/ML, Generative AI & MLOps

• Design and implement AI/ML and Generative AI solutions on GCP using Vertex AI, BigQuery, Cloud Storage, Dataflow, Pub/Sub, Cloud Run, and related GCP-native services.
• Build production-grade machine learning pipelines for data preparation, model training, validation, evaluation, deployment, monitoring, retraining, and lifecycle management.
• Develop Generative AI and Retrieval-Augmented Generation (RAG) solutions, including enterprise search, document intelligence, AI assistants, summarization, semantic search, embeddings, vector search, and knowledge-management applications.
• Design scalable ingestion, transformation, chunking, embedding, indexing, and retrieval pipelines for structured and unstructured enterprise data.
• Implement MLOps practices using Vertex AI Pipelines, Model Registry, model endpoints, Terraform, GitHub, Cloud Build, and CI/CD pipelines.
• Establish standards for model versioning, experiment tracking, data and feature validation, automated testing, deployment approvals, rollback, and environment promotion.
• Implement monitoring for model performance, data drift, latency, reliability, inference cost, response quality, retrieval accuracy, and GenAI risks such as hallucination and prompt injection.
• Ensure responsible AI, data privacy, security, governance, access control, auditability, and human-…