Senior Applications Developer

Marsh McLennan•Dallas, TX

1d•Hybrid

About The Position

As a Sr. Applications Developer at Mercer, you will be at the forefront of our AI-driven transformation, shaping the future of intelligent solutions within our organization. You will champion best practices in AI development, foster innovation, and serve as a technical leader guiding teams to deliver impactful AI powered products. This role requires a hands-on leader who is passionate about building scalable, reliable, and secure AI systems, while mentoring engineers and collaborating across disciplines to embed AI into our core offerings. This position emphasizes a product-focused approach, integrating AI solutions that align with organizational goals, and promoting a culture of continuous learning and innovation in AI technologies.

Requirements

Proven experience of developing enterprise level Software Solutions with AI integrations
Proven experience leading AI-focused engineering teams and delivering AI-driven products or solutions.
Strong understanding of AI/ML frameworks such as TensorFlow, PyTorch, LangFlow, or similar.
Extensive hands-on experience with machine learning model development, deployment, and monitoring.
Proficiency in programming languages such as Python, JavaScript, TypeScript, C#, or others relevant to AI development.
Experience with cloud platforms (AWS, Azure, GCP) for deploying AI solutions at scale.
Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines for AI workflows.
Knowledge of data engineering, data pipelines, and big data technologies relevant to AI.
Strong understanding of AI ethics, fairness, and security considerations.
Excellent communication skills, capable of translating complex AI concepts to technical and non-technical audiences.
Demonstrated ability to build, mentor, and lead high-performing AI teams.
Deep expertise in AI/ML frameworks (TensorFlow, PyTorch, LangFlow, etc.).
Experience with natural language processing (NLP), prompt engineering, and generative AI models.
Familiarity with middleware and messaging systems such as Kafka, RabbitMQ, or similar.
Experience with cloud-native AI deployment, including serverless architectures and container orchestration.
Strong background in software development best practices, including SDLC, TDD, CI/CD, and security.
Working knowledge of databases (SQL, NoSQL) and data modeling for AI applications.
Experience with modern front-end/back-end frameworks (e.g., React, Node.js, NEST, .NET).
Knowledge of ethical AI practices, model interpretability, and compliance standards.

Responsibilities

Lead and Manage AI Engineering Teams: Oversee one or more teams or projects, providing technical guidance, mentorship, and fostering a culture of innovation and excellence in AI development.
AI Solution Design & Development: Work closely with Architects to design, develop, and deploy scalable AI models and solutions, including machine learning, natural language processing, and prompt engineering, ensuring alignment with business objectives.
AI Integration & Optimization: Collaborate with data scientists, data engineers, and product teams to integrate AI models into existing products and new initiatives, optimizing for performance, usability, and ethical considerations.
Research & Innovation: Stay abreast of the latest advancements in AI, machine learning, and related fields. Drive experimentation with emerging AI frameworks, tools, and methodologies to maintain a competitive edge.
Code Quality & Best Practices: Write and review high-quality, maintainable code, leveraging AI development tools such as GitHub Copilot, and promote practices like TDD, CI/CD, and modular design.
AI Model Lifecycle Management: Oversee the end-to-end lifecycle of AI models—from data collection and preprocessing to training, validation, deployment, and monitoring—ensuring robustness and compliance.
Cross-Functional Collaboration: Work closely with product managers, solutions architects, and other stakeholders to define AI requirements, develop prototypes, and deliver production-ready solutions.
AI Governance & Ethics: Advocate for responsible AI practices, including fairness, transparency, and security, ensuring models adhere to organizational and regulatory standards.
Thought Leadership & Community Engagement: Contribute to internal and external AI communities, sharing insights, best practices, and strategic direction for AI initiatives.
Foster a Culture of Learning: Mentor engineers in AI, machine learning, prompt engineering, and related disciplines, promoting continuous skill development and innovation.
Implement rigorous validation protocols to ensure model accuracy, robustness, and fairness.
Develop and maintain testing frameworks for AI models, including unit tests, integration tests, and performance benchmarks.
Establish KPIs and metrics to evaluate model performance in production environments.
Maintain version control for datasets, models, and code to ensure reproducibility.
Incorporate security best practices to prevent model exploitation or adversarial attacks.
Conduct regular risk assessments related to AI system deployment.
Integrate automated testing into CI/CD pipelines to catch issues early.