GCP AIML & GenAI Head of Engineering 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: 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: GCP AI/ML & GenAI - Head of Engineering

Experience: 15 Years

Location: Remote (USA)

Primary Focus: AWS GCP Migration Data Engineering Data Architecture Engineering Leadership & 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-review processes are incorporated into AI/ML and GenAI solutions.
• Partner with Data Science Analytics Product BI Security and US-based stakeholders to identify prioritize and deliver high-value AI/ML and GenAI use cases.

2. GCP Data Platform Architecture

Architect and implement scalable enterprise data platforms on GCP.

Design Data Lake and Lakehouse architectures using GCS and BigQuery.

Define Bronze Silver and Gold/Atomic data layers.

Design scalable data ingestion transformation and consumption frameworks.

Establish standards for data modeling partitioning clustering and storage.

Design multi-tenant and multi-location data architectures.

Define schema-on-read and schema-on-write strategies.

3. AWS Data Platform Expertise

Analyze and optimize existing AWS data platforms before migration.

Work with:

Amazon S3

AWS Glue

AWS Glue Data Quality

Amazon Redshift / Redshift Serverless

Amazon Athena

AWS Step Functions

AWS DMS

AWS Lake Formation

Understand existing AWS ETL/ELT pipelines data models workloads and dependencies.

Identify equivalent or improved GCP services for each AWS workload.

Prepare technical mapping and migration plans between AWS and GCP services.

4. GCP Streaming & Real-Time Data Engineering

Architect real-time data pipelines using:

Google Pub/Sub

Dataflow / Apache Beam

BigQuery

Cloud Storage

Design high-volume event ingestion enrichment and transformation pipelines.

Implement event-driven architectures and appropriate delivery guarantees.

Optimize streaming pipelines for latency throughput and scalability.

Design BigQuery streaming ingestion patterns.

Implement monitoring logging and alerting for real-time workloads.

5. ETL / ELT & Data Processing

Design and implement scalable batch and real-time ETL/ELT pipelines.

Migrate AWS Glue-based pipelines to appropriate GCP services.

Develop transformation frameworks using:

Python

PySpark

SQL

Dataflow / Apache Beam

BigQuery

dbt

Design CDC pipelines and real-time ingestion patterns.

Build orchestration workflows using Cloud Composer / Airflow.

Optimize data processing jobs and query performance.

6. AWS to GCP Migration Leadership

Lead the end-to-end migration of enterprise data platforms from AWS to GCP.

Assess existing AWS architecture data pipelines workloads dependencies and operational processes.

Define the target-state GCP architecture and migration roadmap.

Develop migration strategies for:

Amazon S3 Google Cloud Storage

Amazon Redshift BigQuery

AWS Glue Dataflow / Dataproc / BigQuery

AWS Step Functions Cloud Composer / Workflows

AWS DMS GCP-native CDC solutions

Amazon Athena BigQuery

Identify opportunities to modernize AWS workloads rather than performing a simple lift-and-shift migration.

Define migration phases technical dependencies risks and rollback strategies.

Lead architecture reviews and technical design discussions.

7. Data Modeling & BigQuery

Design enterprise data models for analytics and reporting.

Define dimensional normalized and denormalized data models.

Develop multi-tenant data structures.

Design BigQuery partitioning and clustering strategies.

Optimize BigQuery SQL and query execution.

Design data models supporting both real-time and batch workloads.

Work closely with BI and Analytics teams to create scalable consumption models.

8. Data Governance Security & Quality

Establish data governance and data quality standards across the GCP platform.

Implement automated data quality checks and validation frameworks.

Establish data lineage metadata and ownership standards.

Ensure appropriate security controls across all GCP data layers.

Implement:

IAM

Least-privilege access

Encryption

Service accounts

Network security

Data access policies

Work with governance and security teams to ensure compliance requirements are met.

Experience with Dataplex Data Catalog and data lineage is preferred.

9. DevOps Infrastructure & Automation

Lead infrastructure automation using Terraform.

Build repeatable and secure GCP infrastructure deployments.

Implement CI/CD pipelines for data engineering workloads.

Work with:

Terraform

Git

GitHub

Cloud Build

CI/CD pipelines

Automate data pipeline deployment testing and infrastructure provisioning.

Establish Dev QA UAT and Production deployment standards.

10. Performance & Cost Optimization

Lead performance optimization initiatives across GCP data workloads.

Optimize:

BigQuery query performance

Partitioning and clustering

Dataflow pipelines

Spark workloads

Cloud Storage

Streaming workloads

Analyze AWS workloads and determine the most cost-effective GCP architecture.

Develop cloud FinOps and cost optimization strategies.

Establish performance benchmarks and SLAs for critical workloads.

11. Engineering Management & Team Leadership

Lead and mentor a team of Data Engineers Senior Data Engineers and Technical Leads.

Provide technical direction and establish engineering standards.

Conduct architecture and code reviews.

Define technical roadmaps and engineering priorities.

Break complex migration requirements into actionable deliverables.

Track engineering progress risks dependencies and delivery milestones.

Promote best practices around coding testing CI/CD security and documentation.

Mentor engineers on GCP data architecture and modern data engineering practices.

12. Stakeholder & Client Management

Act as the primary technical point of contact for US-based stakeholders.

Work closely with Business Product Data Science BI and DevOps teams.

Translate business requirements into scalable technical solutions.

Present architecture decisions migration strategies and technical roadmaps.

Communicate technical risks dependencies timelines and trade-offs.

Collaborate with business teams to define operational and analytical KPIs.

• 15 years of experience in GCP Data Engineering Data Architecture Cloud Engineering AI/ML Engineering or related technology leadership roles.
• 5 years of strong hands-on GCP Data Engineering Experience.
• 3 years of strong hands-on AI/ML & Gen AI Experience.
• Proven experience delivering AWS-to-GCP migration projects.
• Strong experience designing enterprise Data Lake and Lakehouse platforms on GCP.
• Strong hands-on experience with BigQuery Google Cloud Storage Dataflow Pub/Sub Cloud Composer Dataproc IAM and Terraform.
• Experience migrating AWS data workloads pipelines and platforms to GCP.
• Strong knowledge of AWS and GCP service mapping migration patterns modernization strategies and cloud architecture best practices.
• Experience designing building and deploying AI/ML solutions on GCP using Vertex AI.
• Hands-on experience with Generative AI LLM-based applications RAG architectures embeddings vector search prompt engineering and enterprise AI assistants.
• Strong understanding of MLOps including model training model registry CI/CD/CT model deployment monitoring retraining governance and rollback strategies.
• Experience implementing secure and responsible AI solutions including data privacy model evaluation access controls auditability and governance.
• Expert-level SQL and strong Python and PySpark skills.
• Strong data modeling data warehousing batch processing and real-time data engineering experience.
• Experience with Terraform Git GitHub Cloud Build CI/CD pipelines and infrastructure automation.
• Experience managing and mentoring data engineering and cross-functional technical teams.
• Strong communication skills with experience working with US-based stakeholders.

Preferred Qualifications

Google Cloud Professional Data Engineer certification.

• Google Cloud Professional Machine Learning Engineer certification.
• Experience with Vertex AI Agent Builder Vertex AI Search Gemini models on Vertex AI or enterprise Generative AI platforms.
• Experience with dbt Apache Airflow Kafka Apache Spark Kubernetes Cloud Run and API-driven architectures.
• Experience with Dataplex Data Catalog data lineage metadata management data governance master data management and data-quality frameworks.
• Experience supporting enterprise or regulated environments with strong data privacy security compliance audit and governance requirements.

Required Technical Skills:

AIML & GenAI Technology Skills

AI/ML: Python PyTorch TensorFlow Scikit-learn NLP Deep Learning ML AlgorithmsGenerative AI: GenAI LLMs GPT Gemini Claude Llama Prompt Engineering Fine-tuning
RAG: RAG Embeddings Vector Databases Semantic Search Hybrid Search Reranking
LLM Frameworks: LangChain LlamaIndex LangGraph Hugging Face Transformers
Agentic AI: AI Agents Agentic Workflows Tool/Function Calling Multi-Agent Systems MCP
MLOps: MLflow Kubeflow Model Registry Model Deployment Monitoring CI/CD
GCP / Vertex AI: Vertex AI Vertex AI Studio Gemini Vertex AI Pipelines Model Garden Vector Search
AI Application Development: Python FastAPI Flask REST APIs SQL Docker Kubernetes
Cloud & Data: GCP/AWS/Azure BigQuery Dataflow Spark Databricks Data Lakes
AI Evaluation & Security: LLM/RAG Evaluation Ragas LangSmith Guardrails AI Governance & Security

Data Engineering Skills

Cloud Platforms: GCP AWS BigQuery GCS AWS S3 Pub/Sub AWS KinesisData Engineering: Advanced Python Expert SQL PySpark Apache Spark ETL ELT CDC
Data Processing: Batch & Streaming Event-Driven Architecture Data Pipeline Development & Optimization
Data Platforms: Enterprise Data Lake/Lakehouse Medallion Architecture Data Warehousing Data Modeling
Data Modeling: Dimensional Modeling Multi-Tenant Modeling Schema-on-Read/Schema-on-Write
Modern Data Stack: dbt Apache Airflow/Cloud Composer Dataproc Dataflow/Apache Beam
AWS Data Services: AWS Glue Redshift EMR Lambda Kinesis Athena CloudWatch
GCP Data Services: BigQuery GCS Pub/Sub Dataflow Dataproc Dataplex Data Catalog
DevOps & Infrastructure: Terraform Git/GitHub Cloud Build CI/CD Infrastructure as Code
Data Governance & Quality: Data Lineage Metadata Management Data Quality Monitoring OpenLineage

• Lead AWS-to-GCP cloud transformation & AIML GenAI initiatives.
• Work on Data Lakehouse and analytics modernization.
• Flexible remote work.
• Exposure to global customers.
• Collaborative innovation-driven culture.
• Continuous learning and certification.

Lead transformative AI/ML & GCP innovations as Head of Engineering at Naveera Tech. Join a global team to revolutionize data into business value. Remote role USA-based. Apply today!