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About This Role
POSITION SUMMARY:
We are seeking an Sr AI Engineer to join our Information Technology team at our corporate office in Springfield, MO.
The Sr AI Engineer serves as a senior technical leader responsible for architecting, scaling, and governing enterprise AI platforms and solutions aligned with the enterprise AI roadmap. This role leads the design and operationalization of advanced AI systems, including generative AI, agentic workflows, retrieval\-augmented generation (RAG), and intelligent automation capabilities that drive measurable business impact across the enterprise.
This position provides technical leadership across AI engineering initiatives, establishes enterprise AI standards and best practices, mentors engineering teams, and partners closely with business, data, security, and platform leaders to ensure scalable, secure, and production\-ready AI ecosystems.
This position requires working onsite in our Springfield, MO headquarters.
ESSENTIAL FUNCTIONS:
- Lead enterprise AI architecture decisions, reference patterns, and platform strategies across multiple business domains.
- Collaborate with stakeholders to identify AI\-driven automation and insight opportunities and define success metrics/acceptance criteria.
- Translate business needs into technical requirements and design end\-to\-end AI workflows, including data sourcing, orchestration, and integration points.
- Ensure data readiness by assessing availability and quality across enterprise and third\-party sources; partner with Data Engineering to design and validate pipelines that produce high\-quality, AI\-ready datasets with data contracts, lineage, and schema\-drift detection.
- Perform data preparation and transformation in SQL or Python when needed for AI workflows.
- Conduct data quality assessments, establish validation rules, maintain data lineage and data contracts, and implement schema\-drift detection with automated gates.
- Design, develop, and deploy LLM\-based, RAG, agentic AI, and generative AI solutions using modern ML/LLM frameworks and cloud AI services.
- Contribute to and implement enterprise PromptOps and LLMOps practices including evaluation pipelines, prompt governance, structured outputs, guardrails, rollback strategies, and automated testing.
- Build and operate autonomous and semi\-autonomous AI workflows with auditable actions, human\-in\-the\-loop approvals, feature flags, and operational safeguards.
- Implement model lifecycle management with experiment tracking, model registry, retraining, embedding/vector\-index versioning, rollback, and monitoring.
- Lead evaluation and selection of foundation models, embedding strategies, vector retrieval architectures, and inference optimization techniques.
- Integrate AI solutions via APIs and event\-driven architectures using versioned, backward\-compatible contracts with semantic versioning and deprecation policies.
- Apply MLOps and LLMOps best practices for scalable, observable, and secure deployments including containerization, orchestration, CI/CD, and model lifecycle management.
- Implement automated testing including unit, integration, regression, evaluation, and contract testing for AI systems and services.
- Partner with Data Scientists and engineering teams to productionize AI and ML solutions into scalable enterprise systems.
- Provide technical mentorship and code/design reviews for AI Engineers, Data Engineers, and software development teams.
- Ensure responsible AI governance including RBAC/IAM, secrets management, PII minimization/redaction, audit logging, explainability, and compliance with governance and privacy standards.
- Lead implementation of observability frameworks including tracing, telemetry, hallucination detection, token/cost monitoring, dashboards, and alerting.
- Establish service\-level objectives (SLOs), operational standards, reliability metrics, and incident response processes for AI platforms.
- Research and evaluate emerging AI technologies such as multimodal models, vector databases, agentic AI, and AI infrastructure platforms.
- Contribute to AI standards, reusable frameworks, engineering documentation, and enterprise best practices.
- Participate in on\-call rotation and operational support activities as needed.
- ALL OTHER DUTIES AS ASSIGNED
EXPERIENCE/QUALIFICATIONS:
- Minimum Degree Required: Bachelors Degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field (or equivalent experience).
- 5 years of experience in AI engineering, machine learning systems, distributed systems, or enterprise AI platform development.
- 3 years designing and deploying enterprise\-scale LLM, RAG, or agentic AI solutions in production environments.
- Demonstrated experience leading architecture and delivery of scalable AI platforms or mission\-critical AI systems.
- Deep proficiency in Python and modern AI/ML frameworks.
- Experience deploying LLM/RAG systems with enterprise data including evaluation frameworks, guardrails, and structured\-output validation.
- Strong experience with vector databases, semantic retrieval systems, and embedding optimization strategies.
- Solid understanding of SQL, data modeling, and data preparation for AI consumption.
- Experience with major cloud platforms (AWS, Azure, GCP) and enterprise data platforms/warehouses such as Snowflake.
- Experience with API development, containerization, orchestration platforms such as Kubernetes, and CI/CD automation.
- Strong understanding of AI governance, security, compliance, and responsible AI practices.
- Experience mentoring engineers and leading cross\-functional technical initiatives.
KNOWLEDGE, SKILLS, AND ABILITY:
- Strong systems architecture and platform engineering expertise.
- Ability to lead technical strategy and influence architectural direction across teams.
- Strong analytical and problem\-solving skills with an end\-to\-end ownership mindset.
- Ability to translate complex AI concepts into actionable business outcomes.
- Excellent communication and collaboration skills across technical and business teams.
- Expertise in designing scalable, resilient, and maintainable AI systems.
- Proficiency with Git\-based version control and collaborative development workflows.
- Familiarity with Agile methodologies and iterative delivery models.
- Understanding of AI feasibility, ROI, and value realization within enterprise environments.
- Experience mentoring engineers and fostering engineering best practices.
- Commitment to responsible and ethical AI development aligned with company standards.
TRAVEL REQUIREMENTS:
- N/A
PHYSICAL REQUIREMENTS:
Regularly sits and works on a computer.
Occasionally stands and walks.
Seldom/never lifts up to 50 lbs.
INDEPENDENT JUDGEMENT:
Performs duties within scope of general company policies, procedures, and objectives. Analyzes problems and performs needs assessments. Uses judgment in adapting broad guidelines to achieve desired result. Regular exercise of independent judgment within accepted practices. Makes recommendations that affect policies, procedures, and practices.
Full Time Benefits Summary:
Enjoy discounts on retail merchandise, our restaurants, world\-class resorts and conservation attractions!
- Medical
- Dental
- Vision
- Health Savings Account
- Flexible Spending Account
- Voluntary benefits
- 401k Retirement Savings
- Paid holidays
- Paid vacation
- Paid sick time
- Bass Pro Cares Fund
- And more!
Bass Pro Shops is an equal opportunity employer. Hiring decisions are administered without regard to race, color, creed, religion, sex, pregnancy, sexual orientation, gender identity, age, national origin, ancestry, citizenship status, disability, veteran status, genetic information, or any other basis protected by applicable federal, state or local law.
Reasonable Accommodations
Qualified individuals with known disabilities may be entitled to reasonable accommodation under the Americans with Disabilities Act and certain state or local laws.
If you need a reasonable accommodation for any part of the application process, please visit your nearest location or contact us athrcompliancebasspro.com.
Bass Pro Shops
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Bass Pro Shops, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Bass Pro Shops AI Hiring
Bass Pro Shops has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Springfield, MO, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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