GCP AI/ML & GenAI - Head of Engineering @ Naveera Tech, USA - Remote Work
for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide which sections have content.
We have: About Naveera Technology LLC (company description) could be "About the role"? Actually "About the role" is typical heading for role overview. We have "Position Overview". Could map to "About the role". Also we have "Key Responsibilities" -> Responsibilities. Then we have "Tasks" which seems like qualifications/requirements. Also there is "Preferred Qualifications". Could combine into Requirements/Qualifications/Skills.
We need to preserve all substantive info. No boilerplate. Remove navigation etc. The text includes greeting and LinkedIn etc - those are not part of job posting? Probably we should remove greeting and LinkedIn line as it's not part of posting? The instruction: remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. Greeting and LinkedIn contact maybe considered not part of posting? It's part of the message but not job description. Probably we should exclude it. Keep only the job posting content: About Naveera Technology LLC, Job Title etc, Experience, Location, Primary Focus, Position Overview, Key Responsibilities, Tasks (which includes experience requirements), Preferred Qualifications.
We must not invent sections like Benefits, Pay, Schedule if not present. There's no mention of benefits, pay, schedule. So we omit those sections.
We need to output only HTML fragment, no /. Use for each section heading we include.
We need to use - for any list of duties/requirements/benefits. For prose use
.
We need to preserve all substantive info; do not invent or summarize away detail.
Let's extract content:
About Naveera Technology LLC paragraph(s):
"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"
That's a block.
Job Title line: "Job Title: GCP AI/ML & GenAI - Head of Engineering". We should not repeat as heading. Could include as
Job Title: GCP AI/ML & GenAI - Head of Engineering
maybe.
Experience: "Experience: 15+ Years". Location: "Location: Remote (USA)". Primary Focus: "Primary Focus: AWS → GCP Migration | Data Engineering | Data Architecture | Engineering Leadership & AI/ML & GenAI". Could combine into a with those details.
Position Overview paragraph(s): "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: enumerated 1-12 with detailed bullet points. We need to output as
- each.
Tasks: appears to be a list of qualifications: "Tasks 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."
That is a long sentence; we can split into list items for each requirement. The instruction: use
- for any list of duties/requirements/benefits. So we should break each requirement into
- . We need to preserve all detail; we can split by sentences.
Preferred Qualifications: "Preferred Qualifications Google Cloud Professional Data Engineer certification. Google Cloud Professional" cut off. We'll include what's there: maybe two items: Google Cloud Professional Data Engineer certification. Google Cloud Professional (maybe incomplete). We'll just include as list items.
We must not include heading for Pay/Benefits/Schedule.
Thus sections: About the role (company overview + position overview?), Responsibilities, Requirements (maybe combine Tasks and Preferred Qualifications as Requirements). Could also have Qualifications and Skills separate but we have limited headings. We could use Requirements for the tasks list, and Skills for maybe technical skills? But we have many details; better to have one Requirements section containing both tasks and preferred qualifications as list.
But we need to follow instruction: "Prefer a few substantive sections over many thin ones." So maybe three sections: About the role, Responsibilities, Requirements.
Let's craft:
About the role
About Naveera Technology LLC: ... (the company description)
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
Position Overview: ... (the paragraph)
Then Responsibilities:
Responsibilities
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- for any list of duties/requirements/benefits. For prose use
. We need to preserve all substantive info; do not invent or summarize away detail. Let's extract content: About Naveera Technology LLC paragraph(s): "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" That's a block. Job Title line: "Job Title: GCP AI/ML & GenAI - Head of Engineering". We should not repeat as heading. Could include as
Job Title: GCP AI/ML & GenAI - Head of Engineering
maybe. Experience: "Experience: 15+ Years". Location: "Location: Remote (USA)". Primary Focus: "Primary Focus: AWS → GCP Migration | Data Engineering | Data Architecture | Engineering Leadership & AI/ML & GenAI". Could combine into awith those details. Position Overview paragraph(s): "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: enumerated 1-12 with detailed bullet points. We need to output as
- each.
Tasks: appears to be a list of qualifications: "Tasks 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."
That is a long sentence; we can split into list items for each requirement. The instruction: use
- for any list of duties/requirements/benefits. So we should break each requirement into
- . We need to preserve all detail; we can split by sentences.
Preferred Qualifications: "Preferred Qualifications Google Cloud Professional Data Engineer certification. Google Cloud Professional" cut off. We'll include what's there: maybe two items: Google Cloud Professional Data Engineer certification. Google Cloud Professional (maybe incomplete). We'll just include as list items.
We must not include heading for Pay/Benefits/Schedule.
Thus sections: About the role (company overview + position overview?), Responsibilities, Requirements (maybe combine Tasks and Preferred Qualifications as Requirements). Could also have Qualifications and Skills separate but we have limited headings. We could use Requirements for the tasks list, and Skills for maybe technical skills? But we have many details; better to have one Requirements section containing both tasks and preferred qualifications as list.
But we need to follow instruction: "Prefer a few substantive sections over many thin ones." So maybe three sections: About the role, Responsibilities, Requirements.
Let's craft:
About the role
About Naveera Technology LLC: ... (the company description)
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
Position Overview: ... (the paragraph)
Then Responsibilities:Responsibilities
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- each.
Tasks: appears to be a list of qualifications: "Tasks 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."
That is a long sentence; we can split into list items for each requirement. The instruction: use