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About This Role
Overview
Point Predictive is redefining fraud detection and risk decisioning for lenders through large\-scale consortium data, machine learning, and real\-time systems. We are seeking an AI Engineer to design, build, and operationalize AI\-powered applications and intelligent workflows that leverage the capabilities of AWS Bedrock and Snowflake Cortex AI. This is a hands\-on engineering role at the intersection of applied AI, data infrastructure, and product delivery — building the systems that make our risk intelligence smarter, faster, and more actionable for lenders across the financial industry.
In this role you'll work closely with Data Science, Data Engineering, Product, and backend engineering teams to move AI capabilities from prototype to production. The ideal candidate has deep, practical experience building LLM\-powered applications on cloud platforms, a strong foundation in data engineering, and the instincts to ship reliable, secure, observable AI systems in a regulated environment.
Responsibilities
- Design and build production AI applications using AWS Bedrock and Snowflake Cortex AI, including retrieval\-augmented generation (RAG) pipelines, LLM\-powered workflows, agents, and semantic search systems
- Integrate foundation models (Claude, Titan, Llama, Mistral, and others available via Bedrock) into Point Predictive's products and internal tooling, selecting the right model for each task based on performance, cost, and compliance requirements
- Build and maintain AI pipelines using Snowflake's native intelligence capabilities — Cortex LLM functions, Cortex Search, ML classification, and Document AI — to surface actionable signals from structured and unstructured data
- Develop and maintain vector stores, embedding pipelines, and document ingestion workflows that power semantic retrieval and context assembly for LLM applications
- Implement AI agent frameworks and multi\-step reasoning workflows using AWS Bedrock Agents, Bedrock Knowledge Bases, and associated tool\-use and orchestration patterns
- Partner with Data Science to integrate model outputs, risk scores, and feature signals into LLM context windows, enabling AI systems that reason over Point Predictive's proprietary data
- Instrument AI systems with observability, evaluation frameworks, and guardrails — tracking latency, accuracy, hallucination rates, and cost across models and pipelines
- Manage prompt engineering, versioning, and systematic evaluation of prompt performance across use cases and model versions
- Enforce security, compliance, and data governance controls across all AI systems — ensuring PII handling, model access, and output logging meet financial industry requirements
- Contribute to AI platform infrastructure: IAM policies, VPC configurations, Bedrock service quotas, and Snowflake role\-based access controls
- Stay current on the rapidly evolving landscape of foundation models, AI tooling, and evaluation techniques, and bring relevant innovations to the team
Qualifications
- 5\+ years of software engineering experience, with at least 2 years focused on applied AI, LLM applications, or ML engineering in production
- Hands\-on experience building LLM\-powered applications on AWS Bedrock — including Knowledge Bases, Agents, Guardrails, and model invocation via the Bedrock Runtime API
- Hands\-on experience with Snowflake Cortex AI — Cortex LLM functions (COMPLETE, CLASSIFY\_TEXT, EXTRACT\_ANSWER, SUMMARIZE), Cortex Search, and Document AI
- Strong Python skills with proficiency in AI/ML libraries and cloud SDKs (Boto3, Snowflake Connector, LangChain or similar orchestration frameworks)
- Experience designing and building RAG architectures, including chunking strategies, embedding model selection, vector store management, and retrieval optimization
- Solid understanding of prompt engineering principles, few\-shot learning, chain\-of\-thought reasoning, and structured output techniques
- Experience with data pipelines and transformations in Snowflake — writing efficient SQL, building dbt models, and working with semi\-structured data formats
- Familiarity with AI evaluation methodology: building eval datasets, measuring retrieval quality, tracking model performance over time, and managing regression
- Experience with AI safety and guardrail patterns — output filtering, PII redaction, content moderation, and input/output logging for compliance
- Understanding of cloud security fundamentals — IAM, KMS encryption, VPC networking — in the context of AI service deployments
- Strong communication skills and ability to explain AI system behavior, limitations, and tradeoffs to non\-technical stakeholders
- Experience in financial services, lending, insurance, or fraud detection is a strong plus
- Familiarity with additional AWS AI services (Comprehend, Textract, SageMaker) and Snowflake ML features is a plus
- Comfort leveraging AI\-assisted development tools (e.g., Claude Code) to accelerate your own engineering productivity
What Success Looks Like
- Production AI applications that are reliable, observable, and trusted by internal teams and customers
- LLM workflows that deliver measurable accuracy and business value — with clear evals to prove it
- RAG pipelines that surface the right context at the right time, reducing hallucination and improving decision quality
- AI systems that meet compliance, security, and data governance requirements without friction
- Snowflake and Bedrock capabilities deeply integrated into Point Predictive's data and product stack
- Clear documentation and reproducible prompt libraries that the team can build on
- Models selected, deployed, and tuned with a clear\-eyed view of cost, latency, and risk tradeoffs
Why This Role
- Build AI systems that directly power fraud detection and risk decisioning across major U.S. lenders
- Work at the frontier of applied LLM engineering — Bedrock and Snowflake Cortex are production tools, not experiments
- High\-impact, high\-visibility role during a critical phase of AI integration across the company
- Collaborate with an experienced data science and engineering team with deep domain expertise in financial risk
- Shape how Point Predictive builds AI — tooling, architecture, and evaluation standards are still being defined
Education
Bachelor's or Master's in Computer Science, Data Science, or a related technical field (Preferred)
Pay: $115,000\.00 \- $125,000\.00 per year
Benefits:
- 401(k)
- Dental insurance
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Vision insurance
Application Question(s):
- This is an in office position, will you commute to the office every day.
- Our Core Values are 1\) Be the Expert, 2\) Get it Done and 3\) Pitch in. Give specific details on how you have embodied these in the past and how you will continue to honor these in the future.
Ability to Commute:
- San Diego, CA 92101 (Required)
Work Location: In person
Salary Context
This $115K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Point Predictive, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($120K) sits 45% below the category median. Disclosed range: $115K to $125K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Point Predictive AI Hiring
Point Predictive has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $125K - $125K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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