Consultant - Python Agentic AI

Job Summary

This role demands an experienced Agentic AI Developer to design, build, and deploy autonomous AI agents capable of reasoning, planning, and executing complex, multi-step workflows within a fast-paced, agile environment.

Responsibilities

• Design, develop, and test autonomous AI agents and multi-agent systems to solve complex business problems.
• Write clean, scalable, and maintainable production-grade code using Python.
• Deploy, manage, and scale AI architectures and agentic workflows securely on Google Cloud Platform (GCP).
• Integrate Large Language Models (LLMs) with external APIs, cloud databases, and enterprise software toolchains.
• Build and optimize advanced Retrieval-Augmented Generation (RAG) pipelines and manage vector databases.
• Implement robust evaluation frameworks, strict grounding techniques, and anti-hallucination guardrails to ensure enterprise-grade reliability.
• Monitor agent performance, debug complex AI reasoning loops, and optimize systems for latency and cost.
• Collaborate closely with product managers, data engineers, and stakeholders to translate business requirements into technical AI solutions.

Required Skills

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related technical field.
• Proven hands-on experience building and deploying generative AI applications in a production environment.
• Advanced proficiency in Python programming with 7-9 years of experience, including asynchronous programming and API development.
• Experience with GCP services, specifically Vertex AI, Google ADK, Cloud Run, Cloud Functions, and BigQuery.
• Deep understanding of LLM capabilities, advanced prompt engineering, and context window management.
• Practical experience with Gen AI orchestration and agentic frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI.
• Strong foundational knowledge of vector search technologies, embeddings, and semantic search methodologies.

Desirable Skills

• Experience designing complex multi-agent architectures where AI models collaborate to solve tasks.
• Background in implementing MLOps pipelines, CI/CD for machine learning, and automated model evaluation.
• Solid understanding of data security, privacy compliance, and AI governance best practices.
• Experience with containerization technologies like Docker and Kubernetes.