Head of Engineering – GCP AI/ML, GenAI

Naveera Technology LLC

. Lead large-scale AWS-to-GCP data platform migration strategy and execution .

Posted 9/24/2026full-timeRemote • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in GCP Data Engineering and AI/ML solutions, with a strong focus on data platform migration, architecture design, and implementation of responsible AI practices. Proven ability to lead cross-functional teams and deliver scalable, high-performance data solutions.

Highest-signal resume keywords

GCP Data EngineeringAWS-to-GCP MigrationAI/ML Solutions DevelopmentMLOps ImplementationData Lake and Lakehouse Architecture

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLPySparkBigQueryDataflowTerraformVertex AIGenerative AIData ModelingETL/ELT

Soft Skills

Strong Communication SkillsTeam LeadershipMentoring

Tools & Technologies

GCPAWSApache AirflowCloud ComposerGitDockerKubernetesCloud BuildData CatalogDataplex

Certifications & Qualifications

Google Cloud Professional Data EngineerGoogle Cloud Professional Machine Learning Engineer

Industry Keywords

Data GovernanceData PrivacyCloud ArchitectureData QualityModel Evaluation

Tech Stack

Tools & technologies

AirflowAmazon RedshiftApacheAWSAzureBigQueryCloudDockerETLFlaskGoogle Cloud PlatformKafkaKubernetesPySparkPythonPyTorchScikit-LearnSparkSQLTensorflowTerraform

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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 & GenAI 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
Google Cloud Professional Data Engineer certification preferred
Google Cloud Professional Machine Learning Engineer certification preferred
Experience with Vertex AI Agent Builder, Vertex AI Search, Gemini models on Vertex AI, or enterprise Generative AI platforms preferred
Experience with dbt, Apache Airflow, Kafka, Apache Spark, Kubernetes, Cloud Run, and API-driven architectures preferred
Experience with Dataplex, Data Catalog, data lineage, metadata management, data governance, master data management, and data-quality frameworks preferred
Experience supporting enterprise or regulated environments with strong data privacy, security, compliance, audit, and governance requirements preferred
Required technical skills include Python, PyTorch, TensorFlow, Scikit-learn, NLP, Deep Learning, ML Algorithms, GenAI, LLMs, GPT, Gemini, Claude, Llama, Prompt Engineering, Fine-tuning, RAG, Embeddings, Vector Databases, Semantic Search, Hybrid Search, Reranking, LangChain, LlamaIndex, LangGraph, Hugging Face, Transformers, AI Agents, Agentic Workflows, Tool/Function Calling, Multi-Agent Systems, MCP, MLflow, Kubeflow, Model Registry, Model Deployment, Monitoring, CI/CD, Vertex AI, Vertex AI Studio, Vertex AI Pipelines, Model Garden, Vector Search, FastAPI, Flask, REST APIs, SQL, Docker, Kubernetes, GCP, AWS, Azure, BigQuery, Dataflow, Spark, Databricks, Data Lakes, Advanced Python, Expert SQL, PySpark, Apache Spark, ETL, ELT, CDC, Batch & Streaming, Event-Driven Architecture, Data Pipeline Development & Optimization, Enterprise Data Lake/Lakehouse, Medallion Architecture, Data Warehousing, Data Modeling, Dimensional Modeling, Multi-Tenant Modeling, Schema-on-Read/Schema-on-Write, dbt, Apache Airflow/Cloud Composer, Dataproc, Dataflow/Apache Beam, AWS Glue, Redshift, EMR, Lambda, Kinesis, Athena, CloudWatch, GCS, AWS S3, Pub/Sub, Dataproc, Dataplex, Data Catalog, Terraform, Git/GitHub, Cloud Build, Infrastructure as Code, Data Lineage, Metadata Management, Data Quality, Monitoring, OpenLineage

Benefits

Comp & perks

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