Lead Software Engineer – Generative AI

Role Overview

• Perform programming, project management, and development assignments independently; typically handle the most complex aspects of systems work.
• Lead complex projects spanning multiple system components and domains.
• Design prototypes and POCs to demonstrate solution feasibility and value. Provide architecture solutions.
• Research, design, build, and train innovative applications of LLMs to solve complex real-world problems.
• Work with custom datasets, utilizing techniques like chunking and embeddings, to train and fine-tune models.
• Engage in all phases of the product lifecycle, including technical requirements definition, project planning and dependency management, system architecture and development.
• Collaborate with cross-functional teams to ensure smooth deployment and integration of AI solutions.
• Apply advanced knowledge and skills to solve highly complex engineering problems.
• Work independently on critical systems or infrastructure components used by multiple applications.
• Drive application development focused on delivering business value.
• Mentor and guide software engineers, providing technical assistance and leadership.
• Maintain high standards of software quality through best practices.
• Demonstrate leadership on medium to large-scale projects impacting strategic priorities.
• Adapt to shifting priorities and timelines using strong analytical and problem-solving skills.
• Effectively prioritize, delegate, and execute tasks in high-pressure environments.
• Stay updated with the latest advancements in AI and machine learning technologies.

Requirements

• Good exposure in AI/ML/NLP/LLM technologies
• Strong experience with LLM APIs (OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI)
• Proficiency in prompt engineering, RAG implementation, and Vector databases
• Strong knowledge of Generative AI techniques for summarization, chatbots, and data embedding using AWS, Microsoft Azure OpenAI, and open-source LLM models
• Experience with AWS services (ECS, KDA, Kinesis, OpenSearch, DynamoDB, CloudWatch, Lambda, API Gateway, etc.)
• Expertise in ML model deployment, AWS SageMaker Studio, Generative AI models, LLM vector stores, and LLM observability
• Hands-on experience with ML methodologies, advanced analytics tools (R, Python), and frameworks (TensorFlow, PyTorch, Keras)
• Knowledge of Natural language processing (NLP) for text generation projects like text parsing, sentiment analysis, and the use of transformers like GPT (generative pre-trained transformer) models
• Ability to translate innovative ideas into POC/POT and pilot/reference production deployments
• Strong analytical and problem-solving skills with a passion for exploring new technologies
• Proven experience in data engineering (batch and streaming) and API development frameworks
• Strong foundation in machine learning, deep learning, and computer science
• Good knowledge in any of the cloud-based platforms and services, such as Azure or AWS.

Tech Stack

• AWS
• Azure
• Cloud
• DynamoDB
• Keras
• Python
• PyTorch
• Tensorflow

Benefits

• Professional growth opportunities
• Leadership programs
• Celebrating diversity