AI/Machine Learning Engineer Job at Mission Produce® in oxnard
Mission Produce®, oxnard, ca, United States
Job Summary
The AI / Machine Learning Engineer designs, builds, and operates intelligent solutions using Azure AI services, Azure AI Foundry, Copilot Studio, and OpenAI on Azure. This role delivers production‑grade AI systems, including LLM applications, AI agents, forecasting and time‑series models, and lakehouse data products that automate decisions and transform business workflows. You will partner closely with operations, finance, sales, sourcing and IT to translate business problems into AI solutions that improve operational decision‑making. This role will also work with large operational datasets to develop AI‑driven insights and automation.
Essential Duties & 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.
Minimum Qualifications & Requirements
- Experience: 5+ years in software/data engineering or machine learning.
- 2+ years building AI/ML or LLM‑based systems in production environments.
- Azure Stack: Hands‑on with Azure AI services, Azure Foundry, Azure Machine Learning, OpenAI on Azure, and Copilot Studio.
- Lakehouse Expertise: Working knowledge of lakehouse architecture and tools such as Microsoft Fabric, Databricks, and/or Azure Synapse.
- DevOps/MLOps: Proficiency with Git, Azure DevOps (or GitHub), Agile methods (e.g., Jira), and CI/CD pipelines for analytical solutions.
- Agents/Workflows: Proven experience building autonomous agents or AI‑driven workflows with safety and observability.
- LLM Practice: Expertise in prompt engineering, fine‑tuning, RAG, and evaluation frameworks.
- Lifecycle Mastery: Comprehensive understanding from experimentation through deployment, monitoring, and continuous improvement.
Desired Skills
- 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.