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About This Role
### Predictive \& Agentic AI Engineer
Position Overview:
Are you an accomplished AI practitioner with a passion for developing cutting\-edge analytics, predictive machine learning, and multi\-agent generative AI solutions? If you have at least five years of hands\-on experience in data science, combined with proven capabilities in building autonomous agentic workflows within the AWS ecosystem and Enterprise Agent Platforms, we have an exciting opportunity for you!
As a Predictive \& Agentic AI Engineer, you will bridge the gap between traditional predictive modeling and state\-of\-the\-art generative AI architecture. You will lead the development of advanced data\-driven models and design autonomous, multi\-agent workflows that solve complex business challenges, automate intricate workflows, and drive deep insights for our MLS subscribers.
#### Key Responsibilities:
- Work with large and complex real estate data sets to extract meaningful insights and solve a wide array of challenging problems using advanced statistical, machine learning, and large language model (LLM) approaches.
- Apply predictive and quantitative analysis, data mining, and experimentation to develop strategies for our product, reporting, and research teams.
- Innovate, define, understand, design, and build prototypes of traditional ML models and autonomous, multi\-agent AI systems to drive our product roadmap.
- Architect and deploy robust AI Agentic workflows capable of task planning, recursive reasoning, tool usage, and self\-reflection to automate complex real estate processes.
- Hands\-on development of end\-to\-end MLOps and LLMOps pipelines, including dataset curation, model training, prompt engineering, agent trace evaluation, testing, and production deployment.
- Continuously refine and optimize traditional ML models and agentic architectures (e.g., via prompt optimization frameworks like DSPy) to maximize accuracy and minimize hallucinations.
- Develop methodologies for evaluating both predictive model performance and generative agent compliance, utilizing A/B testing, cross\-validation, and LLM\-as\-a\-judge evaluation frameworks.
- Partner with Product, Engineering, Research, and other cross\-functional teams to translate business needs into scalable, secure AI\-driven solutions.
- Stay up\-to\-date with cutting\-edge MLOps, LLMOps, and Agentic AI technologies.
#### Required Skills \& Qualifications:
- Experience: 5\+ years of hands\-on experience in data science, analytics, machine learning, and AI engineering.
- Core Languages: Expert in SQL and advanced SQL, and highly proficient in production\-grade Python.
- Traditional ML Ecosystem: Proficient in frameworks, tools, and libraries such as TensorFlow, PyTorch, scikit\-learn, Pandas, and Jupyter Notebook.
- Agentic AI Frameworks: Proven experience building stateful, multi\-agent workflows using LangChain, LangGraph, CrewAI, LlamaIndex Workflows, or the Microsoft Agent Framework.
- Enterprise Agent Platforms: Hands\-on experience using enterprise\-grade agent development and orchestration platforms, specifically Dataiku (utilizing Dataiku LLM Mesh \& Agent Hub) or Google Cloud's Gemini Enterprise Agent Platform (formerly Vertex AI Agent Builder) to build, run, and govern production\-ready AI agents.
- Integrations \& Protocols: Solid proficiency with building API integrations, advanced Function Calling, and working with the Model Context Protocol (MCP) to seamlessly connect LLMs to external databases and software tools.
- Data \& Knowledge Retrieval: Strong data manipulation, wrangling, and mining skills paired with a deep understanding of Vector Databases (e.g., Pinecone, Qdrant, Milvus) and advanced Retrieval\-Augmented Generation (RAG and GraphRAG).
- AI Security: Deep understanding of LLM vulnerabilities, including prompt injection mitigation, jailbreak prevention, and the implementation of safety guardrails (e.g., Guardrails AI, NeMo Guardrails).
- Cloud \& Platform Expertise: Expert in AWS artificial intelligence/machine learning services, specifically AWS SageMaker and AWS Bedrock, Bedrock AgentCore, alongside Redshift, Comprehend, and Lex.
- Observability: Familiarity with LLM tracing and observability infrastructure (e.g., LangSmith, Langfuse, or Arize Phoenix) to monitor and debug complex agent loops.
- Soft Skills: Strong analytical mindset with excellent communication skills to convey intricate technical concepts and agent behaviors effectively to both engineering teams and non\-technical business stakeholders.
#### Education:
- Bachelor's degree in Computer Science, Data Science, Mathematics, Statistics, or a related quantitative field.
#### Preferred Qualifications:
- Master’s degree or a Ph.D. in a quantitative field is a plus.
- AWS Certified Generative AI Developer – Professional certification or equivalent hands\-on experience building autonomous agentic systems using Amazon Bedrock Agents and Bedrock Flows.
- Experience with serverless deployment architectures, including AWS Lambda or Glue jobs.
- AWS Certified Solutions Architect or AWS Certified Machine Learning Specialty is a plus.
*The salary range for this position is approximately $155,000 to $175,000, based on experience, skills, and qualifications. This position is also eligible for annual performance\-based bonuses. Our comprehensive benefits package includes individual and family health, vision, and dental coverage, 401(k) plan with employer\-matching, and Paid Time Off (PTO) and holidays.* *It is the company's policy to recruit, hire, train and promote individuals, as well as to administer any and all personnel actions, without regard to race, color, religion, age, sex (including gender identity, sexual orientation, and pregnancy), marital status, national origin, disability, genetic information, ancestry, military status or any other unlawfully prohibited characteristic in accordance with applicable laws*
Salary Context
This $155K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Bright MLS, Inc, 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. This role's midpoint ($165K) sits 23% below the category median. Disclosed range: $155K to $175K.
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.
Bright MLS, Inc AI Hiring
Bright MLS, Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in North Bethesda, MD, US. Compensation range: $175K - $175K.
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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