As a TPM, you will oversee the full lifecycle of our AIML initiatives, ensuring that data-driven insights are translated into robust, scalable production systems. You will bridge the gap between complex data science research and practical engineering execution.
With 10+ years of experience and a strong foundation in both Java and Python, you will ensure our AIML platforms remain performant and reliable, focusing on delivering high-quality predictive models and data pipelines that drive business value.
Key Responsibilities
Program Orchestration: Lead the end-to-end lifecycle of the Agentic AI platform, from initial agent design and prompt engineering strategies to production deployment and monitoring. Technical Leadership: Partner with development team to evaluate system design trade-offs (e.g., choosing between Python-based microservices for AI logic and Java for high-throughput orchestration). Data-Driven Execution: Collaborate with Data Science teams to define evaluation frameworks (Evals) for agent performance, accuracy, and "hallucination" rates. Lifecycle Management: Manage the transition of models from R&D (Python/Notebooks) into scalable services (Java/Spring Boot/Cloud-native). Compliance & Ethics: Ensure all AIML solutions adhere to data privacy standards and ethical AI guidelines, managing risks related to data security and regulatory compliance (e.g., GDPR, NIST).
Required Qualifications
Experience: 10+ years of total experience in technical program management or software engineering. Core Languages: Minimum of 6 years of professional experience in both Python and Java. You must be able to read code, conduct technical reviews, and understand the nuances of both ecosystems. Agentic AI Knowledge: Familiarity with agentic frameworks (e.g., LangChain, LangGraph, CrewAI, or AutoGen) and orchestration patterns (ReAct, Plan-and-Execute). Data Science Fluency: Solid understanding of ML fundamentals, vector databases (Pinecone, Milvus), and fine-tuning vs. RAG strategies. Cloud & Infrastructure: Experience with AWS/GCP/Azure AI stacks and deploying models at scale using Docker/Kubernetes.
Preferred Qualifications
Education: Advanced degree (MS or PhD) in Computer Science, Artificial Intelligence, or a related quantitative field.
