Interested in this AI/ML Engineer role at Sinclair Broadcast Group?
Apply Now →Skills & Technologies
About This Role
The AI Solutions Engineer is a key role within the AI team that partners with business teams to turn real operational challenges into practical AI-enabled solutions. This person combines strong business curiosity, consultative problem-solving, and hands-on AI capability in designing, building, testing, and improving sophisticated AI agents and workflows.
They will help teams identify where AI can create meaningful value, translate needs into clear solution designs, and build usable prototypes and production-ready solutions in collaboration with business stakeholders and technical partners.
This position is on-site in Hunt Valley, MD. Candidates must be local or willing to commute.
Key Responsibilities
Partner with the business
- Partner with business units to understand their goals, processes, pain points, and opportunities for improvement.
- Participate in discovery sessions that translate ambiguous business problems into well-defined AI solution requirements.
- Collaborate with the broader AI team to shape solution designs, provide practical feedback on feasibility and user needs, and help prioritize the AI opportunity pipeline based on business value, complexity, risk, and strategic alignment.
- Work with data, technology, security, legal, and risk partners to ensure solutions are reliable, responsible, and appropriate for the business context.
Build and integrate
- Design, build, and iterate AI agents, copilots, automations, and workflow integrations using modern AI platforms and tools.
- Develop agent instructions, tools, knowledge sources, guardrails, and human-review workflows.
- Create prototypes that demonstrate value quickly, then help evolve successful concepts into durable solutions.
Test and improve
- Define success metrics and evaluation approaches before build, so each solution has a clear bar for what good looks like.
- Test AI solutions for accuracy, usefulness, safety, consistency, and user experience; continuously improve them based on feedback and performance data.
- Monitor solutions in production for adoption and accuracy drift; retire or rework anything that no longer meets its success bar.
Enable and share
- Explain AI capabilities, limitations, tradeoffs, and recommended practices in clear, non-technical language.
- Build adoption through training, documentation, office hours, and hands-on support for business users.
- Stay current on AI tools, agent patterns, governance practices, and emerging opportunities; bring relevant ideas back to the organization.
Required Qualifications
- 3+ years of hands-on experience building AI agents, copilots, or automations using generative AI platforms, APIs, low-code tools, or development frameworks, including work with structured and unstructured data, knowledge bases, and retrieval-augmented generation.
- 5-7+ years experience solving business problems through process improvement, consulting, business analysis, operations, or a similar role, with demonstrated ability to work with non-technical stakeholders and build trust across business functions
- Hands-on experience building AI agents, copilots, or automations using generative AI platforms, APIs, low-code tools, or development frameworks, including work with structured and unstructured data, knowledge bases, and retrieval-augmented generation.
- Experience solving business problems through process improvement, consulting, business analysis, operations, or a similar role, with demonstrated ability to work with non-technical stakeholders and build trust across business functions.
- Basic programming or scripting capability, such as Python, JavaScript, SQL, or API integration, with experience in workflow automation, systems integration, or service design.
- Experience evaluating AI systems (test cases, quality measurement, prompt iteration, monitoring) and applying responsible AI practices (privacy, security, model risk, change management).
- Strong written and verbal communication skills, including the ability to make technical topics understandable and actionable.
- Sound judgment about when AI is appropriate versus a simpler solution, and comfort experimenting, learning quickly, and moving concepts to usable solutions.
Preferred Qualifications
- Ability to translate business requirements into solution designs, user journeys, and measurable success criteria.
- Product management experience, including working with product roadmaps and stakeholder prioritization.
- Media or broadcast industry experience, including familiarity with newsroom, sales, or traffic operations, or systems such as WideOrbit or OSI.
- Experience with modern agent frameworks and evaluation tooling, such as LangChain, LlamaIndex, OpenAI Agents SDK, Model Context Protocol, LangSmith, or Braintrust.
- Data analysis and enterprise data platform experience, such as Snowflake, Power BI, or dbt, and SQL beyond basics.
- Prior experience on an internal AI enablement, digital transformation, or innovation team.
What Success Looks Like
The AI Solutions Engineer is a constructive, trusted contributor to the broader AI team, bringing business-unit insight into solution design and helping the organization focus its AI investment on the highest-value opportunities. Within the first year, the AI Solutions Engineer will have delivered several solutions that improve productivity, quality, speed, or customer and employee experience. They will be known as a practical business partner who can turn AI potential into responsible, measurable results.
Core Competencies
- Business problem framing
- Consultative stakeholder partnership
- AI agent and workflow design
- Rapid prototyping and iterative delivery
- Clear communication and enablement
- Responsible AI judgment
- Analytical thinking and prioritization
- Curiosity, adaptability, and ownership
The base salary compensation range for this role is $140,000 to $175,000. Final compensation for this role will be determined by various factors such as a candidate's relevant work experience, skills, certifications, and geographic location. Full time positions are eligible for benefits that include participation in a retirement plan, life and disability insurance, health, dental and vision plans, flexible spending accounts, 15 paid vacation days, 2 paid personal days, 9 paid holidays, 40 hours of paid sick leave, parental leave, and employee stock purchase plan.
EEO AND INCLUSIVITY:
Sinclair is proud to be an equal opportunity employer and a drug free workplace. Employment practices will not be influenced or affected by virtue of an applicant's or employee's race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, disability, genetic information, military or veteran status or any other characteristic protected by law.
About Sinclair:
Sinclair, Inc. (Nasdaq: SBGI) is a diversified media company and a leading provider of local news and sports. The Company owns, operates and/or provides services to 177 television stations in 79 markets affiliated with all major broadcast networks; owns Tennis Channel, the premium destination for tennis enthusiasts; and multicast networks CHARGE, Comet, ROAR and The Nest. Sinclair's AMP Media produces a growing portfolio of digital content and original podcasts. Additional information about Sinclair can be found at www.sbgi.net .
About the Team:
The life-blood of our organization is our people. We have a compelling story, a goal-oriented culture, and we take really good care of people. How good? Here is a glimpse: great benefits, open-door policy, upward mobility and a strong desire to see you succeed. Ready to be part of a winning team? Let's talk.
Please note that this position is not eligible for visa sponsorship, including employer sponsorship for an H-1B visa, OPT-STEM employment, etc.
Salary Context
This $140K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Sinclair Broadcast Group, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($157K) sits 26% below the category median. Disclosed range: $140K to $175K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Sinclair Broadcast Group AI Hiring
Sinclair Broadcast Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cockeysville, MD, US. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.