Full Stack Developer With AI Integration

Arc Full-time

United States

2 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Experienced

Experience required

2 years minimum

Working hours

Regular working hours

Job source

arc.dev

Tech stack

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

+40 more

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

Job 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.

Requirements

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.

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