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.