Artificial Intelligence & Machine Learning Engineer

About

Daily Responsibilities

• Develop and deploy enterprise AI agentic solutions using Python, Java, or TypeScript to automate risk, audit, and cybersecurity processes at scale.
• Contribute to the development of LLMs and RAG systems including output quality benchmarking and model performance monitoring (OpenAI, Cohere, Claude, etc.), vector search (pgvector, Milvus, Pinecone), and context engineering pipelines (LangChain, Semantic Kernel) to ensure AI actions are context-aware, secure, and aligned with real-time enterprise data.
• Support the development of multi-agent systems where autonomous agents collaborate, coordinate state, and handle failures across complex business processes leveraging frameworks such as LangGraph, A2A, or AutoGen.
• Build and maintain data pipelines and context layers (Spark, Databricks, Airflow, SQL/NoSQL, FastAPI, GraphQL) to deliver high-quality, relevant context for AI-driven decisions and automation.
• Contribute to robust SDKs, APIs, and reusable components for risk, regulatory, and security AI automation across cloud and on-premise environments (AWS, Azure, on-prem GPU).
• Integrate AI solutions with enterprise systems and platforms, including GRC, ServiceNow IRM, DevOps (Docker, Kubernetes, GitHub Actions, Jenkins), and production monitoring tools.
• Follow and apply best practices in AI safety, privacy, regulatory compliance, and autonomous system guardrails, including model monitoring, fallback mechanisms, and secure deployment in regulated environments (NIST, SOX).
• Collaborate with cross-functional technical teams, share knowledge, and maintain high-quality technical documentation for both internal and external stakeholders.

Must have skills

• Bachelor’s degree (or equivalent) in Computer Science, Software Engineering, or a related field.
• 2-5 years of software engineering experience with Python and at least one of Java, TypeScript, or Go.
• Hands-on experience deploying LLMs, RAG systems, and agent orchestration frameworks (e.g., LangChain, CrewAI, AutoGen), including vector database configuration (pgvector, Milvus, Pinecone, FAISS) and context engineering for AI workflows.
• Proficient with MLOps/DevOps, containers, Kubernetes, CI/CD pipelines (Docker, GitHub Actions, Jenkins), and cloud development (AWS, Azure).
• Foundational data engineering and integration skills with Spark, Databricks, Airflow, SQL (Snowflake, Postgres), NoSQL (MongoDB), and API design (REST, GraphQL, FastAPI).
• Ability to deliver production-ready AI solutions, drive continuous improvement, and communicate effectively with technical and business stakeholders

Nice to have skills

• Experience with fine-tuning LLMs, prompt engineering, context engineering, and model deployment using HuggingFace or similar platforms.
• Strong mathematical foundations in probability, statistics, and optimization for model design, along with familiarity with distributed ML frameworks (Ray, SageMaker) and real-time pipelines (Kafka, Kinesis).
• Experience with cloud environments, security tooling fundamentals, and observability stacks (Grafana, Prometheus, OpenTelemetry).
• Awareness of regulatory frameworks and IT controls (NIST 800-53, ISO 27001, SOX).

Equal Opportunity Employer

Mindlance is an equal opportunity employer. We are committed to inclusive, equitable, barrier-free recruitment and selection processes, and work environment in accordance with the Accessibility for Ontarians with Disabilities Act (AODA). We will be happy to work with applicants requesting accommodation at any stage of the hiring process.