Key Responsibilities

- Design and develop

Agentic AI systems and autonomous AI workflows.

- Build LLM-powered applications using

Python, LangChain, LangGraph

, or similar frameworks.

- Develop AI agents capable of tool use, planning, reasoning, and multi-step task execution.

- Build and optimize

Retrieval-Augmented Generation (RAG)

solutions.

- Integrate LLMs and AI services through APIs, including OpenAI and Azure OpenAI.

- Develop prompt engineering, function calling, tool integration, and structured-output workflows.

- Work with vector databases and embedding technologies.

- Build scalable APIs and backend services using

Python, FastAPI, or similar frameworks

.

- Integrate AI agents with internal systems, databases, REST APIs, and third-party services.

- Evaluate and improve LLM and agent performance, accuracy, reliability, and latency.

- Implement monitoring, logging, testing, and safeguards for production AI applications.

- Deploy AI applications using cloud platforms such as

AWS, Azure, or Google Cloud

.

- Collaborate with cross-functional teams to understand business requirements and convert them into AI solutions.

- Stay current with developments in

Generative AI, LLMs,



AI agents, and multi-agent systems

.

Required Skills

- Strong programming experience with

Python

.

- Hands-on experience developing

Generative AI or LLM-based applications

.

- Understanding of

Agentic AI / AI Agents / autonomous workflows

.

- Experience with

LangChain, LangGraph, AutoGen, CrewAI

, or similar agent frameworks.

- Experience with

RAG, embeddings, vector databases, and semantic search

.

- Experience working with LLM APIs such as

OpenAI, Azure OpenAI, Anthropic, or Google Gemini

.

- Strong understanding of REST APIs and backend development.

- Experience with databases such as PostgreSQL, MongoDB, or similar.

- Familiarity with Docker and Git.

- Experience with at least one cloud platform:

AWS, Azure, or GCP

.

- Solid problem-solving and analytical skills.

Preferred Skills

- Experience building multi-agent systems

.

- Knowledge of LLM evaluation and observability.

- Experience with prompt engineering and structured outputs.

- Knowledge of model fine-tuning or model adaptation.

- Experience with Kubernetes

📌 Agentic AI Engineer (Canada)
🏢 Spait Infotech
📍 Canada