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About This Role
Responsibilities
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- Establish and maintain a structured prompt library for the company which will cover common use cases (summarization, Q\&A, extraction, code generation, file analysis).
- Apply advanced prompting techniques including chain\-of\-thought, few\-shot examples, role specification, and XML\-structured inputs.
- Design and build RAG pipelines connecting our WMS, EDI logs, SOP repositories, contract data, and other systems.
- Deploy and tune LLM\-powered applications including internal knowledge assistants, client\-facing chat, extend RAG based response repositories, and leverage AI to optimize workflows, processes, and drive system improvements.
- Continuously A/B test prompt variants and document performance benchmarks.
- Serve as the subject\-matter expert on Claude Desktop's file\-handling capabilities, including referencing local PDFs, Word documents, spreadsheets, and code files within prompts.
- Create reusable prompt patterns that work reliably with multi\-file inputs, long\-context documents, and structured data.
- Champion prompt engineering best practices across internal teams including Operations, Control Tower, Business segments, and General Counsel to translate business problems into AI solutions.
- Embed PII handling rules, data residency constraints, jailbreak resistance, and refusal behavior guardrails into production prompt workflows.
- Test for prompt injection risks specific to local file inputs — including malicious content embedded in PDFs, DOCX, or CSV files uploaded through Claude Desktop.
- Build and maintain internal evaluation harnesses to measure prompt quality, consistency, and regression over model updates.
- Other duties as assigned
Qualifications and Job Specifications
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- 3\+ years of software engineering or applied AI experience, with at least 1 year focused on LLM prompt engineering.
- Deep hands\-on experience with Claude, GPT\-4, or similar large language models in production or near\-production settings.
- Background in technical writing, UX wireframing, instructional design, or developer advocacy.
- Prompt engineering discipline including system prompt design, zero\-shot, few\-shot, output validation, hallucination mitigation.
- Experience with adversarial prompt testing, red teaming methodologies, or AI safety evaluation frameworks.
- Production deployment mindset. You monitor what you build, you own uptime, you care about latency and cost.
- Previous experience working with a lean team in a regulated industry, such as pharma, healthcare, government, or financial services is preferred.
- Excellent written communication skills; ability to translate complex technical concepts for non\-technical audiences.
- Proven ability to collaborate cross\-functionally across engineering, product, and customer\-facing teams.
Technical Expertise
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- Direct experience with Claude Desktop, including MCP (Model Context Protocol) server configuration and local file tooling.
- Familiarity with RAG (Retrieval\-Augmented Generation) architecture and vector databases (e.g., Pinecone, Weaviate, pgvector).
- Proficiency in Python or JavaScript for building prompt pipelines, evaluation scripts, and automation tooling.
- Supply chain, logistics, or 3PL domain knowledge including WMS, EDI 850/810/856, DSCSA familiarity.
- Knowledge with Intelligent Document Processing (IDP), OCR pipelines, and handwritten text extraction (AWS Textract, CargoShot, or equivalent).
- Exposure to government or enterprise RFP processes, understanding of compliance documentation, and proposal requirements are nice to have.
Additional Employment Requirements
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- Must be able to successfully pass all preliminary employment requirements (i.e., background check and drug screen)
- Other requirements such as professional licensing.
Physical/Mental/Visual Demands
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- Work is light to medium in nature with frequent walking to perform assigned tasks.
- Work is performed in office.
- Must be able to safely conduct occasional lifting of 25 lbs.
Working Conditions
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- Activities occur in a typical office environment.
*LifeScience Logistics is an equal opportunity employer. Candidates will not be hired based on their race, sex, color, religion or national origin. Reasonable accommodations are available for individuals with disabilities. We proudly support the employment of veterans and welcome applications from protected veterans and all qualified applicants.*
### *\* NO AGENCIES PLEASE \**
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At LifeScience Logistics, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
LifeScience Logistics AI Hiring
LifeScience Logistics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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