Senior Applied AI Engineer

About the Role

Key Responsibilities

• Hands-on role, actively working with teams and leveraging technical experience.
• Supporting the development of core AI platform capabilities.
• Building and managing a high-performance platform engineering team, fostering innovation, collaboration, and technical excellence.
• Applying strong engineering expertise in AI, ML, and modern data technologies to enhance platform functionality.
• Leading the design, development, and scaling of large platforms with a focus on reliability, scalability, and security.
• Driving the development and implementation of GenAI and AI solutions on the AI platform using platform capabilities.
• Providing mentorship and career growth opportunities for technical teams as an inspirational people leader.
• Demonstrating strong behavioral traits of enterprise-level leadership, effectively influencing and attuned to the needs of teams and stakeholders.
• Defining and executing the vision and roadmap for AI solutions on the platform that align with organizational strategic goals and customer needs.

Requirements

• 4 or more years of experience in developing AI, ML, or applied engineering applications, with a strong track record of building AI-driven solutions.
• Hands-on experience with cloud platforms such as Google Cloud, AWS, and Azure.
• Strong understanding of GenAI, machine learning, and related technologies along with business acumen.
• Ability to collaborate and partner with business leaders and IT/Data Science teams.
• Strong problem-solving skills with the ability to guide and mentor engineering teams.
• Experience focused on AI-driven use case development.
• Bachelor's or Master's degree in Computer Science or a related field.
• Strong written and verbal communication skills.
• Candidate must be authorized to work in the US without company sponsorship.

Nice to Have

• Development experience for WebService API with AWS, Google Cloud Platform, and Azure suite of tools and their offerings for AI / GenAI.
• Strong proficiency in embeddings, ANN/KNN, vector stores, quantization, database optimization, and performance tuning.
• Proficiency in customization techniques across various stages of the RAG pipeline, including model fine-tuning, retrieval re-ranking, Hybrid search and multimodal RAG plus.
• Experience building applications using RAG and Summarization patterns, prompt management and Agentic Framework.
• Experience in building Agentic system using frameworks like LangGraph and CrewAI.
• Basic understanding of ML frameworks, i.e., TensorFlow, Anaconda, Scikit-Learn, SageMaker, Agentic AI, and Vertex AI.
• Experience working with Docker, Kubernetes and EC2 environment.
• Experience enabling services such as Amazon Q, MSFT Copilot and similar services to enterprise.
• Familiarity with Python Flask or Spring Boot.

Qualifications

• Bachelor's or Master's degree in Computer Science or a related field.

Benefits & Perks

• The listed annualized base pay range is $117,200 - $175,800, with actual pay varying based on performance, proficiency, and competencies.
• Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition.