Your Role

• Integrate LLMs (Llama, OpenAI, Claude, Mistral, Gemini) and embedding models into applications.
• Implement agentic workflows using LangGraph, AgentCore, Google SDK, applying patterns such as Planner-Executor, Reflection Loops, and Multi-Agent Orchestration.
• Build and optimize data ingestion and retrieval pipelines including preprocessing, chunking, enrichment, embeddings, and vector indexing.
• Integrate coarse-grained controls (data masking, compliance filters, enterprise-wide security checks).
• Prototype in Jupyter Notebooks and transition solutions to production.
• Implement LLMOps practices:
• Instrument pipelines for metrics (latency, cost, hallucination rate, accuracy, compliance violations).
• Build dashboards, alerts, and feedback loops.
• Automate evaluations (A/B testing, prompt regression checks, continuous accuracy testing).
• Collaborate with Architect to integrate knowledge graphs where applicable.
• Deploy and maintain solutions on AWS, GCP, or Azure leveraging GPU/compute environments.

Your Profile

• 4+ years of professional experience, with a minimum of 1 year focused on Generative AI technologies
• Strong programming in Python; working knowledge of JavaScript.
• Experience developing on cloud platforms (AWS, GCP, Azure) with GPU-backed compute.
• Proficiency with Jupyter Notebooks for experimentation.
• Hands-on with LLMs, embeddings, and vector search pipelines.
• Experience implementing agentic workflows with security guardrails.
• Familiarity with knowledge graphs is a plus.
• Proven experience in LLMOps implementation (logging, monitoring, evaluation pipelines, observability tools).
• Strong debugging, problem-solving, and execution focus.