Roles & Responsibilities: • Manage client relationships and confirm project deliverables meet expectations • Work with cross-functional teams to integrate AI into applications • Drive strategic planning initiatives to enhance project outcomes • Uphold exceptional standards of quality and innovation in deliverables • Identify and leverage opportunities for technological advancements • Inspire and motivate teams to achieve excellence in their work What You Must Have • Bachelor's Degree • 10+ years of experience • Designing, training, and deploying machine learning models and Large Language Models (LLMs) into production environments • Developing scalable, cloud-native microservices using tools like Docker and Kubernetes • Building end-to-end AI applications integrated into web, mobile, or API-first platforms • Managing CI/CD pipelines and observability for AI systems using tools such as GitHub Actions and MLfow • Implementing vector databases, Retrieval-Augmented Generation (RAG) pipelines, and orchestration tools like LangChain and LlamaIndex • Building and integrating multi-agent frameworks (e.g., LangGraph, LangFlow) to enable autonomous AI task execution • Translating complex business problems into software-engineered AI solutions aligned with industry standard practices • Deploying on cloud platforms (AWS, GCP, Azure) and leveraging services such as SageMaker, Vertex AI, or Databricks • Being proficient in Python and ML frameworks like PyTorch, TensorFlow, and Hugging Face Transformers • Working in cross-functional engineering environments including front-end (React, Next.js) and • platform teams • Being familiar with LLM providers (OpenAI, Anthropic, Meta, etc.) and working with chat/image based model outputs • Having hands-on experience with agent-based frameworks for intelligent orchestration What Sets You Apart: • Master's Degree in Computer Science, Artificial Intelligence and Robotics, Software Engineering, Data Processing/Analytics/Science, or Machine Learning preferred • Delivering AI applications using RAG, vector databases, and agent-based frameworks • Working with multimodal inputs and outputs • Fine-tuning models on domain-specific data • Applying Responsible AI practices including governance and ethics • Maintaining consistency and quality in model outputs • Contributing to open-source projects or AI/ML publications • Demonstrating versatile development experience across layers