Haley Bridge
AI

Machine Learning Engineer

Haley Bridge · New York, United States

Actively hiring Posted 4 months ago

**AI/ML Engineer, Greenfield projects, Python, Azure, Model Development, Deployment, ML Ops and Platform Ownership, MCP, LLM Product Build, NLP, Time-Series Modeling, Cloud Deployed ML Pipelines, Technology Transformation, Financial Services

Hybrid Role: 3 days a week in the mid-town New York office

You must be based in the US with full rights to work**

My client, a reputable Financial Services organisation, is looking to hire an accomplished AI/ML Engineer to join the business. This position will provide you with the opportunity to work in an entrepreneurial, project-driven, greenfield environment as the firm embarks on a large-scale tech modernisation programme. This is an extremely dynamic organisation – it is essential that you are comfortable working in a fast-paced, nimble fashion and are keen to be pro-active, working closely with both technical and non-technical stakeholders at all levels.

Responsibilities:

This is a business critical, greenfield project, with the objective of building LLM products for both internal stakeholders (MCP) and external clients.

You will work closely with the Development Manager, data engineers and a variety of other stakeholders to implement the required infrastructure and architecture, followed by building the products themselves. You will lead model development across classification, forecasting, anomaly detection and clustering, own deep NLP/LLM workstreams and drive enterprise LLM customization through MCP workflows.

You will also be responsible for Deployment, ML Ops and platform ownership (Azure ML MLflow, Azure functions, CI/CD for ML ETC), feature engineering and data integration (Azure Data Factory, SQL) and overall AI innovation leadership throughout the firm.

Desired Skills:

  • Pro-active, entrepreneurial and progressive mindset – ability to combine in-depth technical experience with exceptional stakeholder communication skills, both technical and non-technical
  • Ability to balance speed with best practice, whilst keeping business and technical stakeholders on-side
  • Deep understanding of NLP + embeddings + LLM architectures, plus evaluation methodology and responsible AI controls – ESSENTIAL
  • Multiple years’ experience building production AI/ML systems, ideally from scratch
  • Deep expertise in Python + ML stack – Tensor-Flow, PyTorch, Transformers/LLM frameworks
  • Proven track record in architecting ML platforms in Azure (Azure ML, MLFlow, containerization, monitoring)
  • Expertise in MCP-based LLM workflows (Azure Model Catalog, fine-tuning pipelines, LoRA adapter management, evaluation/guardrails)
  • Proven experience deploying models to production with reliability, governance and measurable business outcomes
  • Strong desire to work in a role where you take the lead on progressive ideas in this space, working in an extremely fast-paced, multiple project driven environment

Tags & focus areas

Used for matching and alerts on DevFound
Fulltime Ai Engineer Machine Learning Mlops Generative Ai Ai
Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.