Lead Developer – AI/ML Recommendation

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.
• Apply 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 assessment

• Strong software engineering fundamentals, including data structures, algorithms, system design, and clean, maintainable coding practices.
• Deep expertise in designing and building large-scale, distributed systems that are performant, scalable, and reliable.
• Technical leadership skills, including setting engineering standards, driving design decisions, and mentoring other engineers.
• Excellent collaboration and communication skills, with the ability to work cross-functionally and convey complex technical concepts to both technical and business audiences.
• Self-directed and proactive, with strong problem-solving instincts and enthusiasm for tackling new challenges.
• Applied knowledge of machine learning, recommendation systems, ranking, and information retrieval, with the ability to select and apply the right techniques to business problems.
• Proficiency in natural language processing and large language models, including experience designing Agentic AI or LLM-based solutions.
• Skilled at rapid prototyping, experimentation, and outcome validation (e.g., A/B testing, offline evaluation) to inform architecture and scale decisions.
• Strong ability to optimize systems for cost, performance, and throughput across massive data volumes.
• Versatility to contribute across the full stack and quickly ramp on new technologies, domains, and problem spaces.
• Ability to translate ambiguous business requirements into clear technical designs, roadmaps, and deliverables.

Core Competencies

• Build relationships
• Develop people
• Lead change
• Inspire Others
• Think critically
• Communicate clearly
• Create Accountability

• 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

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience building and deploying large-scale distributed systems with Java or Java-related technologies/frameworks.
• 3 years of experience leading ML/LLM design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine-tuning).
• 3 years of experience with software design and architecture.
• Proficiency in one or more programming languages commonly used for large-scale and ML systems (e.g., Python, Java, Scala, or Go).
• Familiarity with cloud platforms (e.g., AWS, GCP, or Azure), modern data infrastructure, and LLM tooling.
• Preferred: experience building and deploying recommendation system models (retrieval, prediction, ranking, embedding) in production, and experience building architectures across different modeling domains.
• Strong problem-solving skills and the ability to translate ambiguous business needs into scalable technical solutions.

Benefits Overview

Benefits are subject to change and may be subject to specific elections, plan, or program terms. This role is eligible for the following:

• Medical, dental & vision
• Hospital plans
• 401(k) Retirement Plan – Pre-tax and Roth post-tax contributions available
• Life Insurance (Company paid Basic Life and AD&D as well as voluntary Life & AD&D for the employee and dependents)
• Company paid Short and long-term disability
• Health & Dependent Care Spending Accounts (HSA & DCFSA)
• Transportation benefits
• Employee Assistance Program
• Tuition Assistance
• Time Off/Leave (PTO, Allegis Group Paid Family Leave, Parental Leave)

Salary Range

• $101,100.00 - $172,100.00
• This position is bonus eligible
• Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors.