AI/Machine Learning Engineer

Mission Produce® is the world's leader in producing, sourcing, distributing, and marketing fresh Hass avocados. The AI/Machine Learning Engineer will design, build, and operate intelligent solutions using Azure AI services to deliver production-grade AI systems that improve operational decision-making and automate business workflows.

Responsibilities:

• Solution Engineering: Design and implement AI/ML solutions with Azure Machine Learning, Azure AI Foundry (AI Studio), OpenAI on Azure, and Copilot Studio—delivering resilient, observable, and cost‑optimized applications
• LLM Applications: Build, fine‑tune, and evaluate LLM‑based applications for internal and customer‑facing use cases (retrieval‑augmented generation, function calling, tool use, guardrails, multi‑turn workflows)
• Data & Modeling: Develop and maintain Python pipelines (ETL/ELT) and ML models; implement robust feature engineering and model monitoring across the ML lifecycle
• Forecasting: Deliver demand prediction, sales forecasting, and operational planning models using classical and machine learning time‑series techniques; establish backtesting, drift detection, and continuous retraining
• Platform Integration: Integrate AI into Power Platform solutions and line‑of‑business apps using Copilot Studio, Azure Cognitive Services, and enterprise connectors
• Autonomous Agents: Build task-oriented AI agents and automation workflows with human-in-the-loop controls, safety constraints, and auditability
• Context & Interoperability: Design context management patterns for AI systems and integrate enterprise data sources such as Fabric OneLake, Synapse, Databricks, SharePoint and Graph
• Lakehouse Architecture: Design scalable data products on Fabric/Databricks/Synapse, including medallion layers, Delta/Parquet formats, vector storage, and streaming ingest for real‑time signals
• MLOps & DevOps: Build CI/CD for models and prompts (Git/GitHub/Azure DevOps), environment provisioning (Terraform/Bicep), automated tests, A/B and canary deployments, and rollbacks
• Observability & Governance: Implement telemetry (App Insights, Prometheus), responsible AI evaluations (fairness, safety, toxicity), RBAC/data classification, and evidence trails aligned to IT governance roles
• Documentation & Enablement: Create runbooks, model cards, data contracts, and playbooks; mentor developers and citizen makers on safe and effective AI use

Requirements:

• 5+ years in software/data engineering or machine learning
• 2+ years building AI/ML or LLM-based systems in production environments
• Hands‑on with Azure AI services, Azure Foundry, Azure Machine Learning, OpenAI on Azure, and Copilot Studio
• Working knowledge of Lakehouse architecture and tools such as Microsoft Fabric, Databricks, and/or Azure Synapse
• Proficiency with Git, Azure DevOps (or GitHub), Agile methods (e.g., Jira), and CI/CD pipelines for analytical solutions
• Proven experience building autonomous agents or AI‑driven workflows with safety and observability
• Expertise in prompt engineering, fine‑tuning, RAG, and evaluation frameworks
• Comprehensive understanding from experimentation through deployment, monitoring, and continuous improvement
• Strong Python development skills and experience with machine learning and LLM frameworks such as PyTorch, TensorFlow, or HuggingFace
• Experience building LLM-powered applications, including prompt engineering, RAG pipelines, and evaluation frameworks
• Familiarity with vector embeddings and semantic search using technologies such as Azure OpenAI or Azure AI Search
• Strong understanding of forecasting and time-series modeling techniques
• Experience building data products and pipelines using Fabric, Databricks, Synapse, or similar lakehouse architectures
• Experience integrating AI systems with enterprise data sources and APIs
• Experience implementing MLOps practices on Azure, including model registry, CI/CD pipelines, automated retraining, and monitoring
• Familiarity with Azure DevOps or GitHub Actions for AI/ML lifecycle automation
• Knowledge of data privacy, security, and responsible AI principles