Artificial Intelligence/Machine Learning Lead

Description :

Role Overview :

We are seeking a highly skilled AI/ML Lead to drive the design, development, and deployment of advanced AI/ML and Generative AI solutions, with a strong focus on Large Language Models (LLMs). This role will lead AI initiatives, build scalable intelligent systems, and align AI capabilities with business outcomes.

Key Responsibilities :

AI/ML & GenAI Leadership :

- Define and execute the AI/ML and GenAI strategy aligned with business goals

- Lead the development of LLM-powered applications (chatbots, copilots, automation tools, etc.)

- Drive innovation using Generative AI, NLP, and deep learning techniques

Model Development & Deployment :

- Design, train, fine-tune, and optimize ML and LLM models

- Build solutions using RAG (Retrieval-Augmented Generation), embeddings, vector databases

- Implement prompt engineering and prompt optimization techniques

- Ensure scalable deployment using MLOps practices

Architecture & Engineering :

- Architect scalable AI systems using cloud platforms (AWS/GCP/Azure)

- Work with microservices, APIs, and distributed systems

- Integrate AI models into production-grade applications

Data & Infrastructure :

- Collaborate with data engineering teams for data pipelines, quality, and governance

- Build and manage feature stores, data lakes, and model pipelines

Team Leadership :

- Lead and mentor a team of Data Scientists, ML Engineers, and AI Engineers

- Drive best practices in model lifecycle management, experimentation, and evaluation

- Conduct code reviews, performance reviews, and hiring

Stakeholder Management :

- Work closely with product, engineering, and business teams

- Translate business problems into AI-driven solutions

- Present insights and AI solutions to senior leadership

Required Skills & Qualifications :

Experience :

- Expertise in Python, ML frameworks (TensorFlow, PyTorch, Scikit-learn)

- Strong experience with :

1. LLMs (OpenAI, LLaMA, Claude, etc.)

2. RAG architectures & vector databases (Pinecone, FAISS, Weaviate, etc.)

3. Prompt engineering & fine-tuning techniques

- Experience in MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI)

- Knowledge of cloud platforms (AWS/GCP/Azure)

- Strong understanding of APIs, microservices, Docker, Kubernetes

Data & Analytics :

- Experience with data preprocessing, feature engineering, and model evaluation

- Understanding of data pipelines, ETL, and big data technologies

Preferred Qualifications :

- Experience in AI-driven products (chatbots, copilots, recommendation systems)

- Knowledge of AI ethics, responsible AI, and governance

- Exposure to multi-modal AI (text, image, audio)