Hands-on GenAI Developer to build and optimize Generative AI applications and pipelines. This role focuses on execution (coding, integration, monitoring, and performance tuning of LLM-powered workflows) under the guidance of the Architect.
Key Responsibilities :
– Strong programming skills in Python for Agent development.
– Integrate LLMs (Gemini, Llama, OpenAI, Claude, Mistral) and embedding models into applications.
– Develop agentic workflows using LangChain, LangGraph, Google SDK and CrewAI applying patterns like Planner-Executor, Reflection Loops, and Multi-Agent Orchestration.
– Build data pipelines for ingestion, preprocessing, chunking, enrichment, embeddings, and vector indexing.
– Implement coarse-grained controls (e.g., data masking, compliance filters, security checks).
– Prototype in Jupyter Notebooks and transition solutions to production environments.
– Apply LLMOps practices: instrumentation, monitoring, evaluation automation, and feedback loops.
– Collaborate with Architects to integrate knowledge graphs where applicable.
– Deploy and maintain solutions on GCP using GPU/compute environments.
– Develop agentic flow using A2A (Agent2Agent) protocol
– Integrate with various tools using MCP (Model Context Protocol)
Required Skills & Experience :
– Must have worked on development of Agents using LangChain, LangGraph and Google ADK.
– Experienced in developing Agentic flows using A2A and integrating with tools using MCP.
– Strong Python skills; working knowledge of JavaScript.
– Experience with GCP and Jupyter Notebooks.
– Hands-on with LLMs, embeddings, vector databases, and agentic workflows.
– Familiarity with security guardrails and compliance in GenAI systems.
– Proven experience in LLMOps (logging, monitoring, evaluation pipelines).
– Knowledge of knowledge graphs is a plus.
– Strong debugging and problem-solving abilities.
Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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