Artificial Intelligence Senior AssociateOverview / 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.
