Artificial Intelligence Senior Associate

About the role

Artificial Intelligence Senior Associate

Location: Dearborn, MI (Hybrid)

Overview / Summary

We are seeking an Artificial Intelligence Senior Associate to develop and deploy production-grade AI applications, algorithms, and intelligent automation solutions. This role focuses on building multi-agent systems, RAG pipelines, data-driven applications, and AI services that solve complex problems, generate recommendations, extract patterns, make predictions, and enable self-service capabilities.

The role involves working across generative AI, natural language processing, deep learning, cognitive automation, intelligent process automation, and related AI technologies, with a strong emphasis on production software engineering, scalability, safety, evaluation, and observability.

Key Responsibilities

Understand business requirements and develop AI algorithms, models, and programs to solve complex problems, generate recommendations, extract patterns, make predictions, interpret sensor data, orchestrate automation, and enable self-service capabilities.

Architect and deploy production multi-agent orchestration systems using modern agent frameworks with state management and checkpointing.

Design and productionize RAG pipelines, including chunking, embeddings, hybrid retrieval, and reranking.

Build data-driven applications that translate data into actionable intelligence through large-scale experimentation.

Develop innovative applications using generative AI, deep learning, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants, and specialized programming.

Research and optimize AI technologies to improve the efficiency and accuracy of data analysis and automation.

Design and implement safe, least-privilege, validated execution of LLM-generated SQL.

Build CI/CD, containerization, and infrastructure-as-code solutions for deploying AI services in cloud environments.

Implement evaluation pipelines and observability/tracing for AI and agent systems, including evaluation datasets, LLM-as-judge scoring, and regression monitoring.

Implement guardrails, prompt-injection defenses, and human-in-the-loop approval checkpoints to support safe and reliable AI outputs.

Design cost and latency optimization strategies, including tiered model routing and caching.

Integrate validated AI outputs with operational systems and reporting pipelines.

Collaborate with data scientists to productionize AI prototypes into scalable, monitored services.

Establish versioning, testing, and safe rollout practices for evolving AI and agent logic.

Required Qualifications

Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.

3+ years of experience building production software systems, including 1–2+ years working on ML/AI or LLM-based applications.

Proven experience designing and deploying multi-agent or multi-service architectures in production.

Strong Python proficiency, including asynchronous/concurrent programming.

Experience with backend frameworks such as FastAPI or Flask.

Hands-on experience with agent orchestration frameworks such as LangGraph, CrewAI, LlamaIndex, or equivalent.

Experience building RAG pipelines, including vector databases, embeddings, chunking strategies, and retrieval evaluation.

Cloud deployment experience, ideally with Google Cloud Platform.

Experience with cloud technologies such as BigQuery, Cloud Run/GKE, Vertex AI, and Pub/Sub, or equivalent AWS/Azure services.

Strong SQL skills and experience with cloud data warehouses.

Experience with containerization and CI/CD, including Docker, Kubernetes, and GitHub Actions/Cloud Build.

Experience building evaluation and observability pipelines for LLM/agent systems, including offline evaluation datasets, LLM-as-judge scoring, and tracing tools such as LangSmith, Langfuse, OpenTelemetry, or equivalent.

Understanding of LLM safety practices, including guardrails, output validation, prompt-injection defense, and safe execution of AI-generated code or SQL.

Strong software engineering fundamentals, including API design, testing, version control, and security best practices.

Experience with Google Cloud Platform.

Preferred Qualifications

Experience with cost optimization and model routing, including tiered pipelines and per-task or per-conversation cost modeling.

Experience deploying agentic systems with human-in-the-loop or multi-checkpoint validation workflows.

Experience with automotive, EV charging, IoT, or connected-vehicle telemetry data.

Familiarity with Model Context Protocol (MCP) or similar standards for tool and data integration across agents.

Experience in a startup or 0-to-1 product environment with evolving requirements.

Master's degree in Computer Science, Software Engineering, or a related field.

Work Arrangement

Hybrid work arrangement with four days per week onsite.