Infosys is seeking a hands-on Gen AI / Agentic AI Lead to drive the development and deployment of next-generation AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks. This role is ideal for a mid-level engineer with strong technical depth, a passion for building, and the ability to lead small teams or workstreams in a fast-paced, innovation-driven environment.
Required Qualifications - Candidate must be located within commuting distance of Mississauga, ON (Canada) or be willing to relocate to the area.
- Bachelor's degree or foreign equivalent required from an accredited institution. Will also consider three years of progressive experience in the specialty in lieu of every year of education.
- Candidates authorized to work for any employer in Canada without employer based visa sponsorship are welcome to apply. Infosys is unable to provide immigration sponsorship for this role at this time
- Bachelor's degree in Computer Science, AI/ML, or related field.
- 5 years of experience in software engineering or data science, with 2-3 years in Gen AI or LLM-based systems.
Preferred Qualifications : - Design, develop, and deploy Gen AI applications using LLMs and agentic frameworks (e.g., LangGraph, AutoGen, Crew AI).
- Strong Python programming skills and experience with ML/AI libraries (Hugging Face Transformers, LangChain, PyTorch).
- Hands-on experience with vector databases (FAISS, Pinecone, Weaviate, Azure AI Search).
- Familiarity with cloud platforms and Gen AI services (AWS, Azure, GCP).
- Experience with REST API development (FastAPI, Flask) and containerization (Docker).
- Solid understanding of AI governance, model safety, and prompt engineering.
- Fine-tune open-source and proprietary LLMs using techniques like LoRA, QLoRA, and PEFT.
- Build and optimize RAG pipelines with hybrid retrieval, semantic chunking, and vector search.
- Integrate Gen AI solutions with cloud-native services (AWS Bedrock, Azure OpenAI, GCP Vertex AI).
- Work with unstructured data (PDFs, HTML, audio, images) and multimodal models.
- Implement LLMOps practices including prompt versioning, caching, observability, and cost tracking.
- Evaluate model performance using tools like RAGAS, DeepEval, and FMeval.
- Collaborate with product managers, data engineers, and UX teams to deliver production-ready solutions.
- Mentor junior engineers and contribute to code reviews, design discussions, and best practices.
- Exposure to agentic workflows and autonomous agents.
- Experience with CI/CD pipelines and DevOps tools (GitHub Actions, Jenkins, Terraform).
- Familiarity with front-end integration (React, Angular, TypeScript) and GraphQL APIs.
- Knowledge of model interpretability, bias mitigation, and human-in-the-loop systems.
- Experience with multimodal models and perception systems (e.g., vision + language).
The job entails sitting as well as working at a computer for extended periods of time. Should be able to communicate by telephone, email or face-to-face. Estimated annual compensation range for the candidate based in the below location will be:
Ontario: $ 92740 to $ 123375
