Artificial Intelligence Engineer

Title - AI Engineer
Type - Contract
Location - Remote

Roles and Responsibilities:

-  Vision & Strategy:  Develop and continuously refine the long-term vision for driving innovation, user-centricity, and strategic alignment with broader business goals.

-  AI & System Architecture:  Lead the design and development of the AI-driven architecture, ensuring scalability, robustness, and cutting-edge technology integration across machine learning, NLP, data pipelines, and automation components.

-  Team Leadership & Mentorship:  Guide and inspire a team of AI engineers, machine learning specialists, and product developers, fostering a collaborative, innovative environment that encourages continuous learning and growth.

- Cross-Functional Collaboration:

Partner closely with stakeholders from product, design, engineering, and business units to ensure the roadmap aligns with business needs and delivers tangible value.

-  Platform

Innovation:  Keep the platform at the forefront of technology by incorporating emerging trends in agentic AI, multi-modal interfaces, generative AI, and autonomous orchestration.

-  Quality & Performance Optimization:  Establish benchmarks for platform performance, reliability, and security. Regularly review and refine core systems to ensure peak performance and user satisfaction.

-  User Experience Focus:  Drive a user-first approach, ensuring that functionality and interface support intuitive and seamless interactions for employees, partners, and customers.

-  Vendor & Technology Management:  Evaluate and manage strategic relationships with third-party vendors, especially in areas like NLP, Microsoft integrations (e.g., Copilot), and cloud infrastructure (Azure), to leverage the best available technology.

Qualifications:

- Education:

Advanced degree (PhD preferred) in Computer Science, AI, Machine Learning, or a related field.

- Experience:

3-5 years of experience in AI/ML

-  AI Expertise:  Deep knowledge of machine learning techniques, NLP, LLMs, and data-driven AI architectures, with hands-on experience designing and deploying complex AI systems.

-  Communication:  Excellent communication skills with the ability to translate complex AI concepts into strategic roadmaps and actionable plans, tailored to both technical and non-technical stakeholders.

-  Strategic Mindset:  Strong analytical and strategic thinking skills, with the foresight to anticipate future AI trends and an understanding of how to apply them to a multi-functional platform.

Tools and Technologies:

-  AI Development Tools:  Proficiency with AI development tools and frameworks such as Azure ML, PyTorch, Tensor Flow, Lang Chain, and transformer-based models.

-  Cloud

Infrastructure:  Experience in using cloud-based AI and data infrastructure, particularly with Azure and Kubernetes.

-  Model Optimization:  Familiarity with tools for managing and optimizing AI models in production (e.g., Milvus for vector search, Graph API for MS integrations).

-  System Design:  Expertise in designing scalable, secure, and fault-tolerant AI systems, including familiarity with microservices, API management, and real-time data processing.

Preferred

Skills:

-  Industry Knowledge:  

Experience with in a large, matrixed organization, preferably in technology or a related industry.

-  Multi-Modal Systems:  Knowledge of systems integrating chat, voice, visual data, and embedded dashboards is a plus.

-  Enterprise AI Deployment:  Familiarity with compliance, governance, and scalability considerations in deploying AI in enterprise environments.

-  Project Management:  Skilled in project planning, prioritization, and resource allocation within an agile development environment.