AI/ML Engineer

We need to output HTML fragment with sections using

for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. The posting includes: Role Summary (could be About the role). Key Responsibilities (Responsibilities). Machine Learning & Data Science Experience (Requirements/Qualifications). NLP & LLM / GenAI (maybe part of Requirements). MLOps & Production Engineering (Requirements). Development & Cloud Technologies (Skills). Preferred / Nice-to-Have Skills (maybe Skills). Soft Skills (Skills). Essential Skills (Skills). Digital Skills (Skills). There's no explicit Benefits, Pay, Schedule. So we should not include those sections. Also location, duration, experience maybe metadata; we can fold into a short

maybe under About the role? The instruction: do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

or drop it. So we can include a short paragraph with location, duration, experience. Probably after the heading About the role we can have a paragraph summarizing role and include location/duration/experience. We must preserve all substantive information; do not invent or summarize away detail. Must not omit details. So we need to include all bullet points etc. We need to use

• for any list of duties/requirements/benefits. So for responsibilities we need list items. For requirements we need list items. For skills we need list items. We need to avoid filler like "commensurate with experience". Not present. We need to output only HTML fragment. Let's structure:

  About the role

  Location: Minnetonka Mills, MN. Duration: 6 months. Experience: 4-6 years. Build, train, tune, and deploy machine learning models. Translate data science experiments and prototypes into scalable, production-ready ML solutions. Develop reliable ML services and pipelines for enterprise production environments.

  Then Responsibilities:

  Responsibilities

  • Translate data science prototypes into production-grade ML services and pipelines.
  • Build training and inference code with: reproducibility, versioning, automated testing.
  • Implement scalable online and offline model serving.
  • Optimize model serving for: batching, latency, throughput, scalability.
  • Integrate ML lifecycle tooling, including: experiment tracking, model registries, deployment automation, model monitoring.
  • Collaborate with Data Engineering teams on feature pipelines and data contracts.
  • Own production ML health, including: drift detection, performance regression, rollback strategies, incident response.
  Now Requirements/Qualifications: The posting has sections: Machine Learning & Data Science Experience, NLP & LLM / GenAI, MLOps & Production Engineering, Development & Cloud Technologies (maybe skills). We could combine into Requirements (maybe Qualifications). But we need to decide headings: we can have Requirements (or Qualifications). The instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can choose either Requirements or Qualifications; but we should not create empty sections. We have content for both maybe. Let's split: Requirements: core experience (ML, NLP, MLOps). Qualifications: maybe education? Not present. So we could just have Requirements and then Skills. Better: Use Requirements for the experience sections (ML & Data Science, NLP & LLM, MLOps). Then Skills for technical skills (Python, TensorFlow, etc.) and preferred/nice-to-have and soft skills. Let's extract details. Machine Learning & Data Science Experience: - Applying data science techniques in a commercial enterprise environment. - Strong understanding of: Supervised Learning, Unsupervised Learning, NLP, Time-Series Forecasting, Statistical Analysis. - Experience with large-scale data and production ML workloads. - Experience with scikit-learn and Databricks. - Strong analytical, quantitative, problem-solving, and critical-thinking skills. NLP & LLM / GenAI: - 3+ years of production experience with NLP, including: Transformers, GPT, Other modern NLP technologies. - 2-3 years of experience applying LLMs to real-world business problems. - Hands-on experience with: RAG (Retrieval-Augmented Generation), Vector Databases, Embeddings. - Experience using vision and speech models in GenAI applications. - Experience working with large amounts of data for NLP and LLM solutions. MLOps & Production Engineering: - 5+ years of software engineering experience. - 2+ years of experience shipping ML models to production. - Strong understanding of MLOps and ML system design. - Experience with: CI/CD, DevOps, Model deployment, Model monitoring, Model versioning. - Understanding of ML production challenges, including: Data leakage, Training-serving skew, Model drift, Performance degradation. Development & Cloud Technologies: - Strong Python development skills. - Experience with ML frameworks: TensorFlow, PyTorch. - Experience with: Docker, Kubernetes, REST APIs. - Experience building APIs for machine learning models. - Experience with scalable model serving and distributed ML workloads. Preferred / Nice-to-Have Skills: - Experience with feature stores. - Experience with model registries. - Experience with model monitoring platforms. - GPU optimization experience. - Distributed training experience. - Responsible AI toolkits and compliance experience. Soft Skills: - Strong written and verbal communication skills. - Ability to present detailed technical analyses to broad audiences. - Organized, self-motivated, and able to work independently. - Strong analytical and problem-solving abilities. Essential Skills (list): - AI/ML Engineering - Python - TensorFlow - PyTorch - NLP - LLMs - RAG - Vector Databases - Embeddings - Databricks - scikit-learn - Docker - Kubernetes - REST APIs - MLOps - CI/CD Digital Skills: - Machine Learning - Natural Language Processing (NLP) - AI & GenAI - Products & Tools We need to avoid duplication. Essential Skills list overlaps with many above; but we must preserve all substantive information, not remove duplicates? The instruction: Preserve ALL substantive information; do NOT invent or summarize away detail. It doesn't say to deduplicate. But we could keep them as is; duplication may be okay but could be considered redundant. However we should keep them as they appear. Might be okay to keep both Essential Skills and Skills sections; but we already have a Skills section for technical skills etc. We could combine but risk losing some info. Better to keep separate sections: Skills (maybe covering Essential Skills, Preferred/Nice-to-Have, Soft Skills) and maybe another heading like Additional Skills? But we only allowed headings from list; we could use Skills for all skill-related content. We can include sub-lists inside Skills: maybe separate
  for Essential Skills, Preferred/Nice-to-Have, Soft Skills, Digital Skills. That would preserve all. Thus:

  Skills

  • Essential Skills: AI/ML Engineering, Python, TensorFlow, PyTorch, NLP, LLMs, RAG, Vector Databases, Embeddings, Databricks, scikit-learn, Docker, Kubernetes, REST APIs, MLOps, CI/CD
  • Preferred / Nice-to-Have Skills: Experience with feature stores, model registries, model monitoring platforms, GPU optimization, distributed training, Responsible AI toolkits and compliance experience.
  • Soft Skills: Strong written and verbal communication skills; ability to present detailed technical analyses to broad audiences; organized, self-motivated, able to work independently; strong analytical and problem-solving abilities.
  • Digital Skills: Machine Learning, Natural Language Processing (NLP), AI & GenAI - Products & Tools
  But we need to keep each item as
  • maybe with sublists? The rule: Use
    • for any list of duties/requirements/benefits. It doesn't forbid nested lists or text inside. We can keep as above. Now we need to ensure we didn't miss any substantive info: The role summary paragraph we included location/duration/experience and role summary. Did we include "Build, train, tune, and deploy machine learning models. Translate data science experiments and prototypes into scalable, production-ready ML solutions. Develop reliable ML services and pipelines for enterprise production environments." Yes. We included responsibilities list. We included requirements (ML & Data Science, NLP & LLM, MLOps). Did we include "Strong analytical, quantitative, problem-solving, and critical-thinking skills." Yes inside ML & Data Science. We included Development & Cloud Technologies inside Skills? Actually we placed Development & Cloud Technologies under Skills as part of Essential Skills? Not exactly; we omitted those specifics. We need to include them somewhere. They are substantive. We could include them under Skills as part of Essential Skills list (they already appear: Python, TensorFlow, PyTorch, Docker, Kubernetes, REST APIs). But we also need to mention "Experience building APIs for machine learning models." and "Experience with scalable model serving and distributed ML workloads." Those are not captured. We could add them as separate bullet under Skills. Let's adjust: Under Skills, we can have subcategories: Technical Skills (including frameworks, tools), etc. Simplify: We'll create a Skills section with multiple
      lists each preceded by a

      heading? But we cannot use headings inside Skills besides

      . We could use

      for subheadings inside Skills. That's allowed: we can have

      Technical Skills:

      then

      etc. That preserves info. Thus:

      Skills

      Technical Skills (Core):

      • Python
      • TensorFlow
      • PyTorch
      • Docker
      • Kubernetes
      • REST APIs
      • Databricks
      • scikit-learn
      • MLOps
      • CI/CD

      Preferred / Nice-to-Have Skills:

      • Experience with feature stores
      • Experience with model registries
      • Experience with model monitoring platforms
      • GPU optimization experience
      • Distributed training experience
      • Responsible AI toolkits and compliance experience

      Soft Skills:

      • Strong written and verbal communication skills
      • Ability to present detailed technical analyses to broad audiences
      • Organized, self-motivated, and able to work independently
      • Strong analytical and problem-solving abilities

      Digital Skills:

      • Machine Learning
      • Natural Language Processing (NLP)
      • AI & GenAI - Products & Tools
      But we also need to include Essential Skills list (AI/ML Engineering, etc.) which overlaps with Technical Skills. We could incorporate AI/ML Engineering as a skill. Add to Technical Skills list. Also we need to include NLP, LLMs, RAG, Vector Databases, Embeddings as skills. They are part of NLP & LLM section but also skills. We should include them under Technical Skills as well. Let's compile a comprehensive Technical Skills list: - AI/ML Engineering - Python - TensorFlow - PyTorch - NLP - LLMs - RAG - Vector Databases - Embeddings - Databricks - scikit-learn - Docker - Kubernetes - REST APIs - MLOps - CI/CD - Experience building APIs for machine learning models (maybe as a skill) - Experience with scalable model serving and distributed