Infosys is seeking a hands-on Gen AI / Agentic AI Engineer to design, develop, and deploy next-generation AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks. This role is ideal for an engineer with strong technical depth, a passion for building AI-powered applications, and experience delivering production-grade GenAI solutions 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.
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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
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Bachelor’s degree in computer science, AI/ML, or related field.
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3–5 years of experience in software engineering, machine learning, or data science, with hands-on experience in GenAI or LLM-based systems.
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Strong Python programming skills and experience with ML/AI libraries (Hugging Face Transformers, LangChain, PyTorch).
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Hands-on experience with vector databases (FAISS, Pinecone, Weaviate, Azure AI Search).\\
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Familiarity with cloud platforms and Gen AI services (AWS, Azure, GCP).
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Experience with REST API development (FastAPI, Flask) and containerization (Docker).
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Solid understanding of AI governance, model safety, and prompt engineering.
Key Responsibilities-
Design, develop, and deploy Gen AI applications using LLMs and agentic frameworks (e.g., LangGraph, AutoGen, Crew AI).
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Fine-tune open-source and proprietary LLMs using techniques like LoRA, QLoRA, and PEFT.
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Build and optimize RAG pipelines with hybrid retrieval, semantic chunking, and vector search.
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Integrate Gen AI solutions with cloud-native services (AWS Bedrock, Azure OpenAI, GCP Vertex AI).
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Work with unstructured data (PDFs, HTML, audio, images) and multimodal models.
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Implement LLMOps practices including prompt versioning, caching, observability, and cost tracking.
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Evaluate model performance using tools like RAGAS, DeepEval, and FMeval.
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Collaborate with product managers, data engineers, and UX teams to deliver production-ready solutions.
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Mentor junior engineers and contribute to code reviews, design discussions, and best practices.
Preferred Qualifications:
- Exposure to agentic workflows and autonomous agents.
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Experience with CI/CD pipelines and DevOps tools (GitHub Actions, Jenkins, Terraform).
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Familiarity with front-end integration (React, Angular, TypeScript) and GraphQL APIs.
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Knowledge of model interpretability, bias mitigation, and human-in-the-loop systems.
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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: $ 72783 to $ 105007
