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Machine Learning Engineer Opt Jobs in Edgewater, FL

ABB

Machine Operator-2

Ormond Beach, FL · On-site

Ensures regular standard maintenance of the equipment, tools set-up, programming activities, and ... Leads their own development journey by seeking knowledge and learning opportunities within the team.

New

ABB

Machine Operator-2

Ormond Beach, FL

Ensures regular standard maintenance of the equipment, tools set-up, programming activities, and ... Leads their own development journey by seeking knowledge and learning opportunities within the team.

New

ABB

Machine Operator-2

Ormond Beach, FL · On-site

Ensures regular standard maintenance of the equipment, tools set-up, programming activities, and ... Leads their own development journey by seeking knowledge and learning opportunities within the team.

ABB

Machine Operator-2

Ormond Beach, FL

Ensures regular standard maintenance of the equipment, tools set-up, programming activities, and ... Leads their own development journey by seeking knowledge and learning opportunities within the team.

New

BNY

Vice President, Full-Stack Engineer

Lake Mary, FL · On-site

$80K/yr

Work with other engineers to elevate technology and follow best practices. Collaborate with the ... analysis, machine learning model deployment, and reporting is highly valued. * Not require ...

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All JobsMachine Learning Engineer Opt JobsMachine Learning Engineer Opt Jobs in Edgewater, FL

Machine Learning Engineer Opt information

See Edgewater, FL salary details

$28.5K

$116.4K

$174.9K

How much do machine learning engineer opt jobs pay per year?

As of Jul 10, 2026, the average yearly pay for machine learning engineer opt in Edgewater, FL is $116,399.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,700.00 and $140,100.00 per year, depending on experience, location, and employer.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

What is the difference between Machine Learning Engineer Opt vs Data Scientist?

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

What are some common challenges Machine Learning Engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.
What cities near Edgewater, FL are hiring for Machine Learning Engineer Opt jobs? Cities near Edgewater, FL with the most Machine Learning Engineer Opt job openings:
Machine Learning Engineer Opt jobs near you
Head of Data, ML & Analytics

Head of Data, ML & Analytics

Village Farms International

Lake Mary, FL • On-site

Full-time

Re-posted 3 days ago

Job description

Village Farms International (Nasdaq: VFF) is a vertically integrated global leader in cannabis and plant-based consumer packaged goods, and sustainable innovation. As one of North America’s longest-standing produce operators built on decades of expertise, we’re a company where growers, innovators, and creators come together to shape the future of high performing plant-based brands, globally.

Joining Village Farms means joining a global team built on purpose, integrity, and ambition. Here, we grow people, ideas and possibilities.


The Opportunity

The Head of Data, ML & Analytics is a newly created executive role accountable for building and leading the enterprise data platform, analytics strategy, and applied machine learning and AI initiatives that accelerate sustainable growth, operational efficiency, and stakeholder value. This leader will establish modern data architecture, governance, and business intelligence capabilities (with deep familiarity in Microsoft Fabric or Azure Synapse and Power BI) to unlock high‑quality insights across farming operations, supply chain, finance, sales, and sustainability. The role partners closely with Technology, Finance, Operations, and Commercial leaders, and reports to the CITO.


What You'll Do

Enterprise Data Strategy & Architecture

  • Define and own the enterprise data strategy, reference architecture, and roadmap, aligning with the company’s technology strategy and sustainability goals.
  • Stand up and evolve the enterprise data platform (Microsoft Fabric or Azure Synapse), integrating core systems and external data sources while enforcing scalable, secure patterns.
  • Drive data modeling, master/metadata management, data quality, lineage, and observability across the data lifecycle.

Analytics & Business Intelligence

  • Institutionalize enterprise reporting and self‑service analytics; standardize executive and functional dashboards in Power BI as the enterprise reporting platform.
  • Partner with business teams to translate questions into analytic products (KPIs, forecasts, optimization models) and deliver them reliably and repeatably.

Applied Machine Learning & Advanced Analytics

  • Build a pragmatic ML portfolio aligned with AI initiatives and focused on measurable outcomes (yield optimization, demand forecasting, inventory/supply planning, pricing, sustainability metrics).
  • Establish model governance and MLOps practices; ensure responsible AI/ML aligned to regulatory, privacy, and cybersecurity standards.

Data Governance, Risk & Compliance

  • Chair/enable the enterprise data governance function (policies, standards, stewardship) to ensure integrity, security, and approved publication of reports/data assets across the organization, consistent with enterprise policy.
  • Collaborate with Security, Audit, and Compliance to meet legal, ESG, and market disclosure obligations.

Platform Engineering & Integration

  • Oversee data platform engineering, pipelines, and integrations; ensure performance, reliability, and cost efficiency across cloud resources.
  • Set and enforce development standards, CI/CD, and test automation for data products.

Leadership, Partnership & Change Management

  • Recruit, develop, and retain a high‑performing team (data engineering, analytics, AI/ML, data governance).
  • Create strong partnerships with Technology (including the CITO’s office), Operations, Finance, and Commercial leaders to prioritize investments and deliver outcomes.
  • Lead change management and data literacy programs to scale adoption of analytics and AI.

Financial & Vendor Management

  • Own the data/analytics budget; optimize cloud and licensing costs (e.g., Microsoft Fabric/Power Platform, Power BI) and manage vendor relationships.


What You Bring


Education:

  • 12+ years in data/analytics leadership with 5+ years building enterprise data platforms and BI at scale.
  • Proven experience with Microsoft Fabric (or Azure Synapse) and Power BI; hands‑on familiarity with data lakehouse patterns, pipelines, and semantic modeling.
  • Demonstrated success establishing enterprise data governance, stewardship, and quality frameworks aligned to business needs.
  • Track record of delivering applied AI/ML solutions with clear business impact and strong model governance.
  • Executive presence; ability to influence senior stakeholders and lead cross‑functional initiatives.

Preferred

  • Industry experience in agriculture, CPG, food systems, or sustainability‑centric businesses.
  • Familiarity with Microsoft Power Platform (Power Automate/Apps) to extend analytics into workflow and decision support.
  • Exposure to ESG reporting, sustainability analytics, and operational excellence in asset‑intensive environments.

Success Metrics (12–18 Months)

  • Platform: Enterprise data platform (Fabric/Synapse) live with priority domains integrated; performance and cost KPIs met.
  • Governance: Data governance operating model, policies, and stewardship roles implemented; quality and approval workflows enforced per enterprise policy.
  • Insights: Executive and functional Power BI dashboards adopted company‑wide; measurable improvements in decision cycle times and forecast accuracy.
  • AI Impact: Portfolio of AI/ML use cases in production with quantified ROI (e.g., yield, waste reduction, inventory turns).
  • Engagement: Improved data literacy and self‑service adoption; positive feedback from business stakeholders.

Work Environment & Travel

  • Based in Orlando, FL with periodic travel to farm/greenhouse, distribution, and corporate locations as needed.
  • Work mode (on‑site/hybrid/remote) aligned to company policy and business requirements.

Compensation & Benefits

  • Competitive base salary, annual bonus, and long‑term incentives commensurate with executive responsibilities; comprehensive benefits; potential relocation assistance.

EEO Statement

We are an equal opportunity employer and value diversity. All employment decisions are based on merit, competence, and business need.


Life at Village Farms

We believe It Takes A Village ® and we mean it.

It shows up in how we support each other, how we innovate, and how we win as a team.

Our values guide every decision we make:

Versatility | Innovation | Leadership | Loyalty | Accountability | Grit | Entrepreneurship

What you do here matters - to our people, our products, and the customers we serve.

We’ve got you covered with company-paid comprehensive benefits and education assistance to support your growth.

Ready to Grow With Us?

If you want to be part of something that has real impact - we’d love to meet you.