Artificial Intelligence Senior Associate

AI Engineer

Employees in this job function are responsible for developing intelligent programs, cognitive applications and algorithms for data analysis and automation, leveraging various AI techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming.

Key Responsibilities:

Skills Required:

• Google Cloud Platform

Experience

Required:

• Bachelor's or Master's degree in Computer Science, Software Engineering, or related field (or equivalent practical experience). 3+ years building production software systems, including 1–2+ years on ML/AI or LLM-based applications. Proven experience designing and deploying multi-agent or multi-service architectures in production — not just notebooks or demos.
• Strong Python proficiency, including async/concurrent programming, and experience with backend frameworks (FastAPI, Flask). Hands-on experience with agent orchestration frameworks — Lang Graph, CrewAI, Llama Index, or equivalent — for building stateful, multi-step, tool-using agent workflows.
• Practical experience building RAG pipelines: vector databases (pgvector, Pinecone, Weaviate, or Qdrant), embeddings, chunking strategies, and retrieval evaluation. Cloud deployment experience, ideally Google Cloud Platform (Big Query, Cloud Run/GKE, Vertex AI, Pub/Sub) or equivalent AWS/Azure services.
• Strong SQL skills and experience with cloud data warehouses. Containerization and CI/CD experience (Docker, Kubernetes, Git Hub Actions/Cloud Build). Experience building evaluation and observability pipelines for LLM/agent systems — offline eval sets, LLM-as-judge scoring, and tracing tools (Lang Smith, Langfuse, Open Telemetry, or equivalent) to track task success, latency, and cost.
• Understanding of LLM safety practices: guardrails, output validation, prompt-injection defense, and safe execution of AI-generated code/SQL (sandboxing, least privilege). Solid software engineering fundamentals: API design, testing, version control, security best practices.

Education Required:

• Bachelor's Degree

Education Preferred:

• Master's Degree

Additional Information:

Hybrid 4 days a week onsite Architect and deploy the production multi-agent orchestration layer (interpreter/orchestrator, NL-to-SQL agent, visualization agent, RCA/RAG agent, report composition agent, notification agent), using modern agent frameworks with state management and checkpointing rather than ad-hoc loops. Design and product ionize RAG pipelines (chunking, embeddings, hybrid retrieval, reranking) grounded in approved schemas, engineering documentation, and historical issue records. Own Big Query integration and enforce safe, least-privilege, validated execution of LLM-generated SQL.

Build CI/CD, containerization, and infrastructure-as-code for deploying agent services on GCP (Cloud Run/GKE, Vertex AI). Implement evaluation pipelines and observability/tracing for every agent (golden datasets, LLM-as-judge scoring, regression alerts) so quality is measurable, not assumed. Implement guardrails, prompt-injection defenses, and human-in-the-loop approval checkpoints to ensure correctness and safety before any output triggers downstream action. Design cost/latency optimization strategies, including tiered model routing (cheap filter models vs.

high-capability deep-dive models) and caching. Integrate validated outputs with operational systems (Salesforce ticketing, driver/site-manager notifications) and report export pipelines (PDF/HTML/spreadsheet). Collaborate with data scientists to product ionize prototypes (anomaly detection, diagnostic agents) into scalable, monitored services. Establish versioning, testing, and safe rollout practices (canary/shadow deployments) for evolving agent logic.