Director of AI Engineering

Director Of Ai Engineering

The Director of AI Engineering is responsible for leading the research, design, development, and deployment of AI solutions as per the business strategy. This role bridges AI research with practical engineering, ensuring that AI models are not only innovative but also scalable, efficient, and production ready.

The director will lead and mentor a team of data scientists and AI engineers to build and operationalize AI-driven products and services. This position will collaborate with various stakeholders and cross-functional teams to integrate these custom-built AI solutions into enterprise systems.

Key Responsibilities:

Collaborate with stakeholders to ensure that technical strategies are in line with ABM's AI roadmap.

Conduct comprehensive technical research on emerging AI/ML models and technologies to pinpoint viable solutions for development, MVP creation, and deployment.

Spearhead MVP development and productization by rapidly prototyping and validating promising AI solutions.

Align AI initiatives with business objectives by offering expert insights on feasibility, scalability, and integration.

Oversee the development of machine learning models, including those for NLP, computer vision, and predictive analytics.

Guarantee that AI models are robust, scalable, and optimized for real-world application.

Lead research into state-of-the-art AI/ML methodologies for business functions or products, such as deep learning, reinforcement learning, and generative AI.

Collaborate closely with DevOps to streamline the deployment of AI models into production environments. Establish MLops practice and guardrails

Implement industry best practices for AI/ML pipelines, including model monitoring, versioning, and periodic retraining.

Ensure seamless integration of AI models with cloud platforms like Azure (Synapse, ADF, Databricks).

Establish and enforce ethical AI frameworks to promote fairness, mitigate bias, and maintain transparency in AI systems.

Work with security teams to protect AI models and data from potential threats.

Partner with key stakeholders to implement and measure the impact of AI solutions across various business functions.

Required Skills & Expertise:

Machine Learning & AI: Deep learning, NLP, generative AI, predictive analytics, reinforcement learning.

MLOps & AI Engineering: Model deployment, cloud-based AI solutions, automation, monitoring, and retraining.

Cloud & Data Platforms: Azure (Synapse, ADF, Databricks), AWS, Google Cloud, Snowflake.

Programming & Frameworks: Python, TensorFlow, PyTorch, Hugging Face, Scikit-learn, SQL.

Expertise and knowledge of OpenAI's GPT models, Gemini, LLaMA, Alteryx.

Big Data & Infrastructure: Spark, Kubernetes, Kafka, data lakes, and scalable AI architectures.

AI Governance & Compliance: Ethical AI, bias mitigation, data privacy laws (GDPR, CCPA).

Preferred Qualifications:

Master's degree in Computer science, Data Science, AI, Machine Learning, or a related field (PhD preferred).

12+ years of experience in AI, data science, or ML engineering.

5+ years in leadership roles, managing AI and data teams.

Experience in deploying AI models at scale in enterprise settings.

Effective communication skills to bridge the gap between AI research, engineering, and business stakeholders.