AI/ML Lead Engineer

Job Summary

We are looking for an experienced AI/ML Lead Engineer to lead the design, development, and deployment of enterprise-grade Artificial Intelligence and Machine Learning solutions.

The ideal candidate should have a strong foundation in traditional Machine Learning, Deep Learning, Generative AI, LLMs, and Agentic AI, along with hands-on development and technical leadership experience.

Key Responsibilities

• Lead the architecture, design, development, and implementation of AI/ML solutions.
• Drive end-to-end Machine Learning initiatives, including data preparation, model development, training, validation, and deployment.
• Design and develop Generative AI applications, AI agents, and multi-agent systems.
• Build and optimize LLM-based applications using RAG, prompt engineering, embeddings, and vector databases.
• Develop predictive models, classification, regression, clustering, and forecasting solutions.
• Work with Deep Learning, NLP, neural networks, and advanced ML algorithms.
• Leverage frameworks such as LangChain, LangGraph, and LlamaIndex for AI application development.
• Integrate AI/ML models with enterprise applications, APIs, and cloud platforms.
• Evaluate and optimize model performance, accuracy, scalability, and cost efficiency.
• Establish MLOps practices for model deployment, monitoring, versioning, and continuous improvement.
• Lead technical discussions, architecture reviews, and mentor AI/ML engineering teams.
• Collaborate with business stakeholders, architects, and product teams to deliver AI-driven solutions.

Required Skills

• Strong programming experience in Python and AI/ML engineering.
• Strong foundation in Machine Learning, Deep Learning, and Statistical Modeling.
• Hands-on experience with ML algorithms, predictive modeling, classification, regression, and clustering.
• Experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and Keras.
• Strong experience in Generative AI, LLMs, and Agentic AI.
• Experience building AI agents and multi-agent architectures.
• Knowledge of RAG, embeddings, vector databases, and prompt engineering.
• Experience with LangChain, LangGraph, or similar AI frameworks.
• Exposure to OpenAI, Azure OpenAI, Anthropic Claude, or other LLM platforms.
• Experience with MLOps, model deployment, monitoring, and model lifecycle management.
• Knowledge of cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Strong technical leadership and stakeholder management skills.