AI Lead

Prospect 33

Toronto

On-site

CAD 120,000 - 160,000

Full time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

Benefits offered by this job

True ownership and impact

Direct collaboration with financial institutions

Culture of technical excellence

Opportunity to shape GenAI strategy

Job summary

A technology-driven firm is seeking a Gen AI Lead / Data Scientist (PhD) to lead AI architecture and initiatives for leading global financial institutions. The role includes end-to-end delivery of Generative AI systems and mentoring engineers. Required qualifications include a PhD in a related field, expertise in Python, SQL, and a strong foundation in machine learning. This position offers true ownership and the chance to impact the firm’s GenAI strategy.

Qualifications

PhD in Computer Science, Machine Learning, AI, or closely related field.
Strong foundation in statistical modeling and applied AI system design.
Deep understanding of algorithms and data structures.

Responsibilities

Lead design and production of next-gen Generative AI systems.
Architect end-to-end AI/ML pipelines from data to model.
Drive advanced GenAI initiatives including LLM-based systems.

Skills

Expert-level proficiency in Python

SQL

PySpark

ML frameworks

Education

PhD in Computer Science or related field

Tools

Databricks

Kubernetes

Terraform

Job description

Gen AI Lead / Data Scientist (PhD) – Prospect 33 (P33.ai)

Location: New York/ Toronto

Employment Type: Full-Time

Work Model: In-House Leadership Role

About Prospect 33

Prospect 33 is expanding its Data, AI & Research Technology (DART) practice and is seeking an exceptional Gen AI Lead / Data Scientist (PhD) to join our team. This is not a vendor or staffing role. You will be a core, in-house member of Prospect 33, owning AI architecture and delivery end-to-end while leading high-impact initiatives for some of the world’s most sophisticated banks and asset managers.

You will sit at the heart of the firm, helping define how generative and predictive AI transform investment research, sales, trading, and risk across global capital markets.

What You’ll Do

Lead the design, development, and productionization of next-generation Generative AI systems and data-driven models.
Architect and own end-to-end AI/ML pipelines, from data ingestion and feature engineering to model training and low-latency inference.
Drive advanced GenAI initiatives including LLM-based systems, RAG pipelines, and agentic AI frameworks.
Provide technical leadership across distributed systems, optimizing Spark and Databricks workloads for performance, latency, and cost.
Establish best practices for MLOps and LLMOps including CI/CD, infrastructure-as-code, experiment tracking, and monitoring.
Mentor engineers and data scientists and serve as a senior technical representative with internal leadership and external stakeholders.

Must-Have Qualifications

PhD in Computer Science, Machine Learning, Artificial Intelligence, Engineering, or a closely related field.
Expert-level proficiency in Python, SQL, PySpark, and modern ML frameworks in production environments.
Deep understanding of algorithms, data structures, distributed systems, and large-scale model training.
Proven track record of translating research into production-grade AI or ML systems.
Strong foundation in statistical modeling, machine learning theory, and applied AI system design.

Strong Advantages

Hands-on experience with Generative AI including LLMs, RAG architectures, agent frameworks, and prompt engineering.
Experience with Databricks, Snowflake, Kubernetes, Terraform, and cloud-native AI platforms.
Exposure to capital markets including investment research, trading, risk, or alternative data.
Experience operationalizing advanced research into scalable enterprise systems.

Why Prospect 33