Principal, AI​/ML Engineer

Job Description:

The Role

We're seeking an exceptional Principal AI/ML Engineer to join our high-impact squad focused on transforming and growing our Financial Investment (FI) sales and platform business. In this role, you'll be at the intersection of cutting-edge AI technology and business strategy, driving innovation through opportunity lead generation, intelligent product recommendations, coverage strategy optimization, and engagement experimentation.

As a T-shaped AI professional, you'll bring deep technical expertise while demonstrating strong business acumen and cross-functional collaboration skills. Critically, we need someone who thinks in systems-understanding how each change ripples through the entire ecosystem-and who approaches every project with a production-ready mindset from day one, not just building shiny demos.

Key Responsibilities

AI/ML Innovation & Implementation

* Design and deploy state-of-the-art AI and ML solutions to accelerate FI business growth with production reliability and scalability as primary considerations

* Develop and optimize Large Language Model (LLM) applications for business use cases that integrate seamlessly into existing systems

* Implement context engineering strategies and prompt optimization techniques

* Build and maintain Retrieval-Augmented Generation (RAG) systems for semantic search and knowledge retrieval

* Research and prototype emerging AI techniques, always evaluating for production viability and system-wide impact

Systems Thinking & Architecture

* Analyze and anticipate how AI implementations affect upstream and downstream systems, data flows, and user experiences

* Design solutions considering scalability, maintainability, observability, and failure modes from the outset

* Evaluate technical decisions through the lens of system-wide performance, cost, and operational complexity

* Collaborate with platform, infrastructure, and application teams to ensure seamless integration

* Document system dependencies, data lineage, and architectural decisions for long-term maintainability

Business Partnership & Strategy

* Collaborate closely with business stakeholders to deeply understand challenges and translate them into technical solutions

* Define and monitor AI performance metrics aligned with business KPIs

* Balance innovation with pragmatism-prioritizing solutions that deliver measurable business value

Data Engineering & Architecture

* Design and implement robust ETL pipelines for both structured and unstructured data

* Build scalable data infrastructure supporting real-time and batch processing needs

* Perform exploratory data analysis to uncover insights and improvement opportunities

* Ensure data quality, governance, and security best practices

Deployment & Operations

* Deploy and manage AI/ML models in cloud environments (AWS) with production SLAs in mind

* Establish monitoring systems for model performance, drift detection, and system health

* Optimize model serving infrastructure for latency, throughput, and cost

* Implement MLOps best practices for continuous integration and deployment

* Build with observability, debugging, and incident response capabilities from the start

The Expertise and Skills You Bring

Education & Experience

* BS/MS in Engineering, Computer Science, Data Science, or related field

* 5-8+ years of software development experience with proven AI/ML project delivery in production environments

* Demonstrated ability to manage multiple concurrent projects in fast-paced environments

T-Shaped Expertise Profile

Deep Technical Skills (the vertical bar):

* LLM & Modern AI:
Hands-on experience with Large Language Models, prompt engineering, context optimization, and fine-tuning techniques

* AI/ML Engineering:
Expert knowledge of statistical models, predictive modeling, time series analysis (regression, classification, clustering, dimension reduction)

* Programming:
Advanced proficiency in Python with object-oriented and functional programming paradigms

Broad Cross-Functional Skills (the horizontal bar):

* Business Acumen:
Ability to understand financial services domain and translate business needs into technical solutions

* Data Engineering:
Production experience with ETL pipeline tools (Airflow, dbt) and big data technologies (Snowflake)

* Deployment & MLOps:
Experience deploying and managing applications in cloud environments (AWS preferred)

* Collaboration:

Strong communication skills to work effectively with technical and non-technical stakeholders

Technical Stack Experience

* Python Ecosystem:
Num Py, Pandas, Scikit-learn, Flask, Pip, Anaconda

* ML/AI Frameworks:
Hugging Face, Lang Chain (or similar)

* Big Data Tools:
Spark, Snowflake

* Cloud Platforms: AWS (Sage Maker, Lambda, EC2, S3, etc.)

* Data Pipeline Tools:
Airflow, dbt, or equivalent orchestration frameworks

* RAG & Vector Databases:

Experience with semantic search, embeddings, and vector stores

Core Competencies

Production-First Mindset:

* Builds production-ready solutions from day one-not prototypes that need to be rebuilt

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