Artificial Intelligence Engineer

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: $130,000.00 - $145,000.00 per year 401(k) 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. #J-18808-Ljbffr Point Predictive