Applied AI/ML Engineer / Data Scientist
Apex Systems · Juno Beach, FL · 1 mo ago
EngineeringContract
We need to output only HTML fragment, no html/body tags, no markdown fences. Use for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections when posting has real specific content. We have:
- Role Overview (we can map to About the role)
- Key Responsibilities (Responsibilities)
- Required Qualifications (Education, Experience, Technical Skills) -> could be Requirements or Qualifications. We need to decide: maybe Requirements for required qualifications, Qualifications for preferred? But we can combine. The instruction: Use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We should include sections that have content.
We have:
- About the role: Role Overview paragraph.
- Responsibilities: list of responsibilities.
- Requirements: Required Qualifications (Education, Experience, Technical Skills). Could be under Requirements.
- Qualifications: Preferred Qualifications.
- Skills: maybe technical skills already covered; but we could have a Skills section for technical skills? However we already used Technical Skills under Required Qualifications. Might be redundant. Better to have Requirements covering required qualifications (education, experience, technical skills). Qualifications covering preferred. Skills maybe not needed if covered. But we could still include Skills if we want to list technical skills separately. However we must avoid thin sections. Let's see: The posting includes "Technical Skills:" under Required Qualifications. So we could put that under Requirements as a list.
Thus sections:
for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections when posting has real specific content. We have:
- Role Overview (we can map to About the role)
- Key Responsibilities (Responsibilities)
- Required Qualifications (Education, Experience, Technical Skills) -> could be Requirements or Qualifications. We need to decide: maybe Requirements for required qualifications, Qualifications for preferred? But we can combine. The instruction: Use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We should include sections that have content.
We have:
- About the role: Role Overview paragraph.
- Responsibilities: list of responsibilities.
- Requirements: Required Qualifications (Education, Experience, Technical Skills). Could be under Requirements.
- Qualifications: Preferred Qualifications.
- Skills: maybe technical skills already covered; but we could have a Skills section for technical skills? However we already used Technical Skills under Required Qualifications. Might be redundant. Better to have Requirements covering required qualifications (education, experience, technical skills). Qualifications covering preferred. Skills maybe not needed if covered. But we could still include Skills if we want to list technical skills separately. However we must avoid thin sections. Let's see: The posting includes "Technical Skills:" under Required Qualifications. So we could put that under Requirements as a list.
Thus sections:
About the role
... role overview paragraph.
Responsibilities
- ... each responsibility.
Requirements
Education: ...
Experience: ...
- ... technical skills list.
Qualifications
- ... preferred qualifications.
Benefits
... benefits overview paragraph.
Pay and Schedule: not mentioned, so omit. We must preserve all substantive information, not invent. Remove boilerplate: cookie/navigation/legal/EEO etc. Remove duplicated lines, application instructions. Also remove the virtual recruiter note, privacy policy, etc. Also remove the equal opportunity employer repeated sections. Also remove the job ID line. Also remove location? Location is substantive: "Location: Juno Beach, Florida (Remote)". Should we include? It's not a heading but we can include in About the role paragraph or as a separate short p. Probably include in About the role paragraph. We need to preserve all substantive information: role overview, responsibilities, required qualifications (education, experience, technical skills), preferred qualifications, benefits overview. Also location. We must not include any filler like "commensurate with experience". Not present. Now craft HTML. We need to ensure proper escaping? Not needed. Let's extract role overview paragraph: "We are seeking an Applied AI/ML Engineer / Data Scientist to develop and operationalize the intelligence layer of an enterprise platform. This role will combine data science, machine learning, generative AI, and software engineering to create intelligent capabilities that help users understand operational information, diagnose issues, identify anomalies, retrieve knowledge, and make better decisions. This position emphasizes taking AI capabilities from experimentation through production deployment and evaluation." Add location maybe after that: "Location: Juno Beach, Florida (Remote)." Now responsibilities list: each bullet from "Key Responsibilities". Let's list them: - Design, develop, evaluate, and deploy AI/ML capabilities. - Develop analytical models for anomaly detection, asset health, forecasting, classification, and other operational use cases. - Develop generative AI and agentic capabilities using enterprise-approved foundation models and AI platforms. - Design prompts, tools, agents, workflows, and orchestration patterns. - Develop Retrieval-Augmented Generation and knowledge-retrieval solutions when appropriate. - Create rigorous evaluation frameworks for LLM and agent behavior. - Establish metrics for model accuracy, relevance, reliability, hallucination, latency, and cost. - Develop guardrails and validation mechanisms for AI-generated responses. - Collaborate with Data Engineering to define training, inference, retrieval, and feature-data requirements. - Collaborate with the Full Stack/Cloud Engineer to deploy AI services into production. - Develop prototypes rapidly while designing solutions that can transition into production. - Monitor model and agent performance and continuously improve deployed capabilities. - Communicate model behavior and analytical findings to engineers, product stakeholders, and operational subject-matter experts. - Stay current with emerging AI, agentic AI, ML, and data-science technologies and assess their applicability. Now required qualifications: Education: "A Master's degree or Bachelor's degree with equivalent experience in Computer Science, Data Science, Engineering, Statistics, Machine Learning, or a related discipline is required." Experience: "A minimum of 4+ years of experience developing machine-learning or advanced analytics solutions is necessary. Experience taking analytical or ML solutions from experimentation into production is also required." Technical Skills: list: - Strong Python skills are required, along with experience with common ML/data-science frameworks and libraries. - Candidates must have a strong foundation in statistics, experimentation, model evaluation, and data analysis. - Experience with cloud-based data and compute environments, APIs, software-development practices, source control, and CI/CD is also needed. - A demonstrated ability to translate business or operational problems into analytical approaches is essential. We can put these as- ...
About the role
We are seeking an Applied AI/ML Engineer / Data Scientist to develop and operationalize the intelligence layer of an enterprise platform. This role will combine data science, machine learning, generative AI, and software engineering to create intelligent capabilities that help users understand operational information, diagnose issues, identify anomalies, retrieve knowledge, and make better decisions. This position emphasizes taking AI capabilities from experimentation through production deployment and evaluation. Location: Juno Beach, Florida (Remote).
Responsibilities
- Design, develop, evaluate, and deploy AI/ML capabilities.
- Develop analytical models for anomaly detection, asset health, forecasting, classification, and other operational use cases.
- Develop generative AI and agentic capabilities using enterprise-approved foundation models and AI platforms.
- Design prompts, tools, agents, workflows, and orchestration patterns.
- Develop Retrieval-Augmented Generation and knowledge-retrieval solutions when appropriate.
- Create rigorous evaluation frameworks for LLM and agent behavior.
- Establish metrics for model accuracy, relevance, reliability, hallucination, latency, and cost.
- Develop guardrails and validation mechanisms for AI-generated responses.
- Collaborate with Data Engineering to define training, inference, retrieval, and feature-data requirements.
- Collaborate with the Full Stack/Cloud Engineer to deploy AI services into production.
- Develop prototypes rapidly while designing solutions that can transition into production.
- Monitor model and agent performance and continuously improve deployed capabilities.
- Communicate model behavior and analytical findings to engineers, product stakeholders, and operational subject-matter experts.
- Stay current with emerging AI, agentic AI, ML, and data-science technologies and assess their applicability.
Requirements
Education: A Master's degree or Bachelor's degree with equivalent experience in Computer Science, Data Science, Engineering, Statistics, Machine Learning, or a related discipline is required.
Experience: A minimum of 4+ years of experience developing machine-learning or advanced analytics solutions is necessary. Experience taking analytical or ML solutions from experimentation into production is also required.
- Strong Python skills are required, along with experience with common ML/data-science frameworks and libraries.
- Candidates must have a strong foundation in statistics, experimentation, model evaluation, and data analysis.
- Experience with cloud-based data and compute environments, APIs, software-development practices, source control, and CI/CD is also needed.
- A demonstrated ability to translate business or operational problems into analytical approaches is essential.
Qualifications
- Hands-on experience developing applications using LLMs.
- Experience with agentic frameworks, tool calling, MCP, or similar AI orchestration technologies.
- Experience with RAG, embeddings, vector search, and knowledge-management architectures.
- Experience implementing systematic LLM evaluation and guardrails.
- Experience with AWS AI/ML services.
- Experience with time-series analytics and anomaly detection.
- Experience with industrial, energy, renewable-generation, BESS, or operational datasets.
- Familiarity with MLOps and model-monitoring practices.
Benefits
Everforth Apex offers a range of supplemental benefits, including medical, dental, vision, life, disability, and other insurance plans that offer an optional layer of financial protection. We offer an ESPP (employee stock purchase program) and a 401K program which allows you to contribute typically within 30 days of starting, with a company match after 12 months of tenure. Everforth Apex also offers a HSA (Health Savings Account on the HDHP plan), a SupportLinc Employee Assistance Program (EAP) with up to 8 free counseling sessions, a corporate discount savings program and other discounts. In terms of professional development, Everforth Apex hosts an on-demand training program, provides access to certification prep and a library of technical and leadership courses/books/seminars once you have 6+ months of tenure, and certification discounts and other perks to associations that include CompTIA and IIBA. Everforth Apex has a dedicated customer service team for our Consultants that can address questions around benefits and other resources, as well as a certified Career Coach. You can access a full list of our benefits, programs, support teams and resources within our ‘Welcome Packet’ as well