Associate Director - AI Engineering

Associate Director AI Engineering (16-26 yrs)

Leadership & Strategy:

• Define and execute the AI engineering roadmap aligned with organizational goals.
• Lead, mentor, and grow high-performing AI engineering teams.
• Collaborate with executive leadership, product management, and business stakeholders to identify AI opportunities and prioritize initiatives.
• Establish engineering best practices, coding standards, and AI governance frameworks.
• Drive innovation by evaluating emerging AI technologies and industry trends.

AI Solution Development:

• Architect, design, and oversee the development of scalable AI and machine learning solutions.
• Lead the implementation of Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and intelligent automation solutions.
• Ensure AI models are production-ready, scalable, secure, and maintainable.
• Guide the development of AI APIs, microservices, and cloud-native AI applications.

Engineering & MLOps:

• Build and manage enterprise AI platforms and MLOps pipelines.
• Establish CI/CD processes for AI model deployment and lifecycle management.
• Implement monitoring, model versioning, drift detection, and performance optimization.
• Ensure AI systems meet security, compliance, and governance requirements.

Collaboration:

• Partner with Data Science, Data Engineering, DevOps, Security, and Product teams to deliver AI-driven products.
• Work with business leaders to translate business requirements into scalable AI solutions.
• Communicate technical concepts effectively to executive and non-technical stakeholders.

Delivery & Operations:

• Oversee end-to-end AI project delivery from ideation to production.
• Manage project timelines, budgets, risks, and resource planning.
• Ensure high availability, reliability, and performance of AI platforms.
• Drive continuous improvement through automation and operational excellence.

Required Qualifications:

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• Master's degree preferred.
• 10+ years of software engineering experience with at least 5+ years leading AI/ML engineering teams.
• Proven experience delivering enterprise AI and machine learning solutions.
• Strong experience managing cross-functional technical teams.

Technical Skills:

Artificial Intelligence:

• Machine Learning
• Deep Learning
• Generative AI
• Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• AI Agents and Agentic AI
• Natural Language Processing (NLP)
• Computer Vision (preferred)

Programming:

• Python
• Java
• Scala (preferred)
• SQL

AI Frameworks:

• TensorFlow
• PyTorch
• Scikit-learn
• LangChain
• LlamaIndex
• Hugging Face Transformers
• OpenAI APIs or equivalent LLM platforms

Cloud Platforms:

• Microsoft Azure
• Amazon Web Services (AWS)
• Google Cloud Platform (GCP)

MLOps & DevOps:

• MLflow
• Kubeflow
• Docker
• Kubernetes
• Jenkins
• GitHub Actions
• Terraform
• CI/CD pipelines

Data Technologies:

• Spark
• Databricks
• Snowflake
• Vector Databases (Pinecone, Weaviate, ChromaDB)
• PostgreSQL
• MongoDB

APIs & Integration:

• REST APIs
• GraphQL
• Microservices Architecture
• Event-Driven Architecture

Leadership Competencies:

• Strategic thinking and execution
• People leadership and coaching
• Stakeholder management
• Executive communication
• Cross-functional collaboration
• Decision-making and problem-solving
• Change management
• Innovation mindset

Preferred Qualifications:

• Experience leading enterprise AI transformation initiatives.
• Knowledge of Responsible AI, AI governance, model risk management, and ethical AI practices.
• Experience building AI platforms for highly regulated industries.
• Experience with AI security, data privacy, and compliance standards.
• Professional certifications in Azure AI, AWS Machine Learning, Google Cloud AI, or equivalent are desirable.

Key Performance Indicators (KPIs):

• Successful delivery of AI initiatives on time and within budget.
• AI solution adoption and measurable business impact.
• Platform reliability and operational efficiency.
• Model performance, scalability, and quality.
• Team engagement, retention, and capability development.
• Engineering productivity and innovation.