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•

SummaryJob Summary:

Connected is a multi-year strategic program to digitally transform Sales, Recruiting, and Marketing capabilities for Allegis Group and its operating companies. Our Recommendation system handle information/data at massive scale and extend well beyond search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing; the list goes on and is growing every day. As a Software engineer

AI ML Recommendation, you will workrole on a specific project critical to Connected Program that responsible for researching, designing, developing, and optimizing innovative solutions on the Recommendation System to enable critical business functions and deliver a great user experience.

We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full stack as we continue to push technology forward.

The Software engineer

AI ML Recommendation operates at the intersection ofRecommendation engine, ML, Agent, data, platforms, and user workflows, validating outcomes quickly, and informing scale decisions.

This role is critical tomodernize the recommendation platform with AI/ML which will help the team to implement Agentic AI solution for business need.

The Software Engineer Lead is responsible for:

Developing new and enhancing existing solutions, refactoring when needed to optimize implementation and reduce technical debt
Lead the design and implementation of recommendation systems, optimize ML infrastructure, and guide the development of model architecture.
Defining team design and development standards and complying with platform standards to best leverage the Recommendation platform
Provide technical leadership from development through execution to deliver high-quality products
Coaching and leading development team members
Engaging with other teams, providing thought leadership and technical expertise within Connected and across the enterprise

ResponsibilitiesEssential Functions:

Research, design, develop, and optimize scalable solutions for the recommendation system that enable critical Sales, Recruiting, and Marketing business functions across Allegis Group and its operating companies.

Lead the modernization of the recommendation platform using AI/ML techniques, including the design and implementation of Agentic AI solutions to meet evolving business needs.
Architect and build large-scale, distributed systems spanning information retrieval, recommendation engines, ranking, and candidate/job matching workflows.
machine learning, natural language processing, and related AI methods to improve recommendation relevance, quality, and user experience.
Own technical direction for assigned projects, translating ambiguous business problems into well-defined technical designs and delivery plans.
Rapidly prototype, validate outcomes, and run experiments (e.g., A/B tests, offline evaluation) to inform model, architecture, and scale decisions.
Optimize systems for performance, scalability, reliability, and cost across large data volumes and high-throughput workloads.
Collaborate cross-functionally with product, data science, platform, enterprise architecture, and operating-company stakeholders to align technical solutions with business priorities.
Provide technical leadership and mentorship to engineers, setting standards for code quality, system design, and engineering best practices.
Stay current with emerging techniques in AI/ML, recommendation systems, and distributed computing, bringing fresh ideas to advance the platform.

Supervisory or Management Responsibility:

Technical leadership across cross-functional teams.
Mentoring engineers on rapid prototyping, experimentation, and pragmatic delivery.
Influencing design and delivery standards without direct authority

Budget Responsibility:

Contributes to licensing projections and vendor assessmentQualificationsMinimum Education and/or

Experience:

Bachelor's degree or equivalent practical experience.
8 years of experience in software development.
3 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production and experience building architecture in different modeling domain.

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