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# Full Stack Developer With AI Integration

- **Company:** Arc Full-time
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Unity 3d, Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon S3, Application Integration Architecture, ARM Architecture, Microsoft Azure, BigQuery, Cloud Computing, Cloud Engineering, Cloud Storage, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Security, Github, Identity and Access Management, Information Retrieval, Python (Programming Language), Machine Learning, OpenAI, Standard Sql, Azure Machine Learning, Azure Data Lake, Software Engineering, TypeScript, Google Cloud, Retrieval-Augmented Generation, Large Language Models, Snowflake, Multi-Agent Systems, Prompt Engineering, Apache Spark, Multi-Cloud, Llamaindex, Apigee, Build Server, Amazon Virtual Private Cloud (VPC), Data Lakes, Kubernetes, Google Cloud Functions, Operational Systems, Machine Learning Operations, Gsuite, Google Gemini, Terraform, Data Pipelines, Docker, Databricks
- **Published:** October 29, 2026
- **Apply:** https://arc.dev/signup?userType=developer&publicRandomKey=pverff3dkr&to=https%3A%2F%2Farc.dev%2Fdashboard%2Fd%2Fverified-jobs%2Fpverff3dkr%2Fapplication

## About the Role

* At least five years of software engineering experience, including two or more years building and deploying production ML or LLM systems.
 * Strong Python skills, working knowledge of SQL, and experience with at least one of TypeScript, Go, or Java.
 * Hands-on Google Cloud experience with Vertex AI and Gemini, BigQuery, Cloud Run or GKE, Cloud Storage, Pub/Sub, IAM, and VPC networking.
 * Ability to provision and configure GCP infrastructure using infrastructure as code, including service accounts and networking.
 * Production LLM engineering experience, including prompt design and optimization, advanced RAG, hybrid or vector retrieval, reranking, tool and API calling, agents, evaluation, and guardrails.
 * Data engineering experience building resilient ETL/ELT pipelines, modeling schemas, managing vector databases, and working with complex unstructured enterprise data.
 * Understanding of batch and streaming ingestion, execution performance, data partitioning, and cost optimization.
 * Working knowledge of infrastructure as code, CI/CD, and containers, such as Terraform, GitHub Actions or Cloud Build, and Docker or Kubernetes.
 * Ability to work through ambiguous requirements and independently drive solutions from problem definition to deployment., * Experience with AWS services such as Bedrock, SageMaker, S3, and Lambda, or with cross-cloud networking and IAM.
 * Experience with Azure OpenAI, Azure ML, Data Lake Storage, or Entra ID.
 * Snowflake experience, including Cortex, Snowpark, secure data sharing, or data movement between Snowflake and BigQuery.
 * Databricks experience with Mosaic AI, MLflow, Unity Catalog, Delta Lake, or Spark.
 * Google Cloud certifications in machine learning, agentic architecture, cloud architecture, or data engineering.
 * Experience with Google ADK, LangGraph, LlamaIndex, or MCP.
 * Background in consulting, solutions engineering, or a startup environment.
 * Experience in regulated industries such as healthcare, financial services, or the public sector, including related compliance requirements such as HIPAA, SOC 2, or GDPR.
 * Strong GCP experience combined with working knowledge of another cloud or data platform.

## Description

* Work with client teams to understand their workflows, data, and technical constraints, then shape AI solutions around measurable needs.
 * Design, build, and deploy LLM-powered applications, multi-agent systems, RAG solutions, and ML pipelines using Vertex AI and Gemini.
 * Develop scalable ETL/ELT pipelines across BigQuery, Cloud Storage, Snowflake, Databricks, Amazon S3, Azure Data Lake, and operational systems.
 * Connect AI solutions with Google Workspace, Apigee, Cloud Run, Pub/Sub, Cloud Functions, and identity services.
 * Design secure multi-cloud architectures, including cross-cloud identity, networking, and data movement.
 * Improve production reliability through evaluation benchmarks, observability and tracing, cost controls, guardrails, and automated CI/CD.
 * Share reusable patterns, components, and product feedback with internal engineering and solutions teams.
 * Explain technical trade-offs, demonstrate progress, and present results to technical and executive audiences.