AI Platform Lead Engineer

About the job

AI Platform Lead Engineer-IN The AI Platform Lead Engineer builds and maintains the systems that support AI and machine learning applications, working to ensure the systems are scalable, efficient, and reliable. Key Responsibilities and Duties • Deploy, manage and scale AI systems, as well as support model activity and deployment infrastructure in cloud environments • Work with infrastructure and application development teams, data engineers, and model owners, to better integrate the AI applications into mainstream enterprise applications. • Put into production the ML models keeping in mind the scalability, optimization, resource availability, and security for the AI solution. Educational Requirements • University (Degree) Preferred Work Experience • 5+ Years Required; 7+ Years Preferred Physical Requirements • Physical Requirements: Sedentary Work Career Level 8IC Generative AI Development • Design and implement Generative AI solutions, including: • RAG (Retrieval-Augmented Generation) pipelines: Build end-to-end systems integrating vector databases (e.g., Pinecone, Weaviate, FAISS), embedding models, and LLMs to enable context-aware, knowledge-grounded responses • Prompt engineering and optimization: Develop robust prompting strategies, templates, and workflows that maximize LLM performance, accuracy, and consistency • LLM fine-tuning and reinforcement learning: Customize foundation models using supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), or parameter-efficient methods (LoRA, QLoRA) to improve performance for domain-specific tasks • Model evaluation and benchmarking: Establish rigorous evaluation frameworks to measure model accuracy, latency, cost, hallucination rates, and task-specific performance metrics; conduct A/B testing and comparative analysis across models and configurations • Observability and monitoring: Implement comprehensive logging, tracing, and alerting systems to track model behavior, prompt-response patterns, token usage, errors, and drift in production environments • Build production-grade AI agents using both low-code platforms and high-code custom implementations, optimizing flexibility, performance, and maintainability • Integrate diverse AI/ML capabilities including natural language processing (NLP), computer vision, document intelligence, and traditional machine learning models as needed to solve complex business problems • Stay current with the rapidly evolving AI landscape, evaluating and adopting new models, techniques, and tools to continuously enhance solution capabilities and maintain competitive advantage Related Skills Business Acumen, Data Preprocessing, Data Science, Innovation, Machine Learning (ML), Market/Industry Dynamics, Predictive Modeling, Programming, Statistics

Requirements

• Business Acumen
• Data Preprocessing
• Data Science
• Innovation
• Machine Learning (ML)
• Market/Industry Dynamics
• Predictive Modeling
• Programming
• Statistics

Qualifications

• University Degree Preferred

Preferred Technologies

• Business Acumen
• Data Preprocessing
• Data Science
• Innovation
• Machine Learning (ML)
• Market/Industry Dynamics
• Predictive Modeling
• Programming
• Statistics

About the company

TIAA Global Capabilities was established in 2016 with a mission to tap into a vast pool of talent, reduce risk by insourcing key platforms and processes, as well as contribute to innovation with a focus on enhancing our technology stack. TIAA Global Capabilities is focused on building a scalable and sustainable organization , with a focus on technology , operations and expanding into the shared services business space. Working closely with our U.S. colleagues and other partners, our goal is to reduce risk, improve the efficiency of our technology and processes and develop innovative ideas to increase throughput and productivity. We are an Equal Opportunity Employer. TIAA does not discriminate against any candidate or employee on the basis of age, race, color, national origin, sex, religion, veteran status, disability, sexual orientation, gender identity, or any other legally protected status.