Agentic Technical Product Delivery Manager

GenAI Technical Product Delivery Manager

Agentic GenAI Technical Product Delivery Manager having expertise to deal with usecases solutions and delivery with LLM code, Agentic Code, business analyst, Architect develops and implements GEN AI architecture strategies, best practices, and standards to enhance AI ML model deployment and monitoring efficiency. Develop architecture, Ai workflow, LLM modeling for GenAI Solutions and tech stacks. This role will focus on the technical development, deployment, and optimization of GenAI solutions, ensuring alignment with business strategies and technological advancements.

Responsibilities

1. Develop cutting-edge architectural strategies for Gen AI components and platforms, leveraging advanced techniques such as chunking, Retrieval-Augmented Generation (RAG), AI agents, and embeddings. Balance build versus buy decisions, ensuring alignment with SaaS models and decision trees, particularly for the PepGenX platform. Emphasize low coupling and cohesive model development.
2. As a LLM Model Developer, engage in the intricate process of fine-tuning Large Language Models, focusing on instruction fine-tuning and domain adaptation. Fine-tune and optimize models: fine-tune pre-trained language models, optimize performance, and reduce latency while maintaining high-quality output. Benchmark model performance and iterate based on findings, ensuring models meet accuracy and efficiency standards.
3. Lead working sessions for Arch Alignment, pattern library development, GEN AI Tools Data Architect alignment, tag new components to reuse (components reuse strategy), patterns of the usecases, reuse components (save efforts, time, money).
4. Define AI architecture and select suitable technologies, develop code, LLM custom models, agentic development with crew ai langgraph, CrewAI, fix issues, with a focus on integrating RAG systems, embedding models, and advanced LLM frameworks. Decide on optimal deployment models, ensuring seamless integration with existing data management and analytics tools.
5. Lead the implementation of LLM operations, focusing on optimizing model performance, scalability, and efficiency. Design and implement LLM agentic processes to create autonomous AI systems capable of complex decision-making and task execution.
6. Work closely with data scientists and AI professionals to identify and pilot innovative use cases that drive digital transformation. Assess the feasibility of these use cases, aligning them with business objectives, ROI and leveraging advanced AI techniques.
7. Gather inputs from various stakeholders to align technical implementations with current and future requirements. Develop processes and products based on these inputs, incorporating state-of-the-art AI methodologies.
8. Audit AI tools and practices, focusing on continuous improvement of LLM ops and agentic processes. Collaborate with security and risk leaders to mitigate risks such as data poisoning and model theft, ensuring ethical AI implementation.
9. Stay updated on AI regulations and map them to best practices in AI architecture and pipeline planning. Develop expertise in ML and deep learning workflow architectures, with a focus on chunking strategies, embedding pipelines, and RAG system implementation.
10. Apply advanced software engineering and DevOps principles, utilizing tools like Git, Kubernetes, and CI/CD for efficient LLM ops. Collaborate across teams to ensure AI platforms meet both business and technical requirements.
11. Spearhead the exploration and application of cutting-edge Large Language Models (LLMs) and Generative AI, including multi-modal capabilities and agentic processes. Oversee MLOps, automating ML pipelines from training to deployment with a focus on RAG and embedding optimization.
12. Engage in sophisticated model development from ideation to deployment, leveraging advanced chunking and RAG techniques, Agentic AI. Effectively communicate complex analysis results to business partners and executives.
13. Have the opportunity to apply your deep expertise in LLM/GenAI technologies. Your main responsibility will be collaborating closely with clients to prototype, build, test, and deploy products powered by GenAI/LLM technology on a large scale. Additionally, you will play a key role in fine-tuning the hyperparameters of LLM models, optimizing their configuration to ensure the overall model performance and enhance the overall model performance to drive positive outcomes for clients.
14. Proactively reduce biases in model predictions, focusing on fair and inclusive AI systems through advanced debiasing techniques in embeddings and LLM training.
15. Design efficient data pipelines to support large language model training and inference, with a focus on optimizing chunking strategies and embedding generation for RAG systems.

Qualifications

• Bachelor's or master's degree in computer science, Data Science, or a related technical field.
• Demonstrated ability to translate complex technical concepts into actionable business strategies. Experience in data-driven decision-making processes.
• Strong communication skills, with the ability to collaborate effectively with both technical and non-technical stakeholders.
• Proven track record in managing and delivering AI/ML projects, with a focus on GenAI solutions, in large-scale enterprise environments.