Data & Ai tech Manager/Data & Ai Tech Manager

Principal Responsibilities Data Management and Extraction · Maintain an inventory of data assets, including databases, data sources, and formats, ensuring that all data is easily accessible and well-organized. · Design and implement processes for data extraction from various internal and external sources, ensuring accurate and timely retrieval of information. Data Integration · Collaborate with other functions to design and implement system integration solutions that ensure seamless data flow between various applications and platforms. · Manage and optimize databases to ensure efficient data storage, retrieval, and processing, supporting the organization’s data needs. Data Analytics and Tools · Collaborate with business units to gather data requirements, providing support for data analysis via reporting tools, system integration, and AI applications. · Work closely with various departments to understand their data needs and provide support for data-related projects and initiatives. · Oversee the selection, implementation, and management of data analytical tools and platforms (e.g., BI tools, data visualization software) to enhance data analysis capabilities across the organization. Agentic Architecture & System Design · Design Multi-Agent Systems: Architect scalable multi-agent orchestration frameworks where specialized agents collaborate to solve complex, multi-step business problems. · Build Advanced RAG Pipelines: Lead the development of next-gen Retrieval-Augmented Generation (RAG) systems that go beyond simple vector search, integrating Knowledge Graphs, hybrid search, and re-ranking models to provide agents with precise, context-aware grounding. · Memory & State Management: Design sophisticated long-term and short-term memory mechanisms (using vector DBs, SQL, or key-value stores) to allow agents to maintain context across long-running sessions and learn from past interactions. Application Engineering & Product Delivery · End-to-End Agent Lifecycle: Own the full software development lifecycle (SDLC) for AI applications, from prototype to production, ensuring agents are not just demos but reliable, latency-optimized products. · Human-in-the-Loop (HITL) Interfaces: Build intuitive UI/UX patterns for human-agent collaboration, including approval gates, intervention points, and feedback loops humans guide or correct agent actions before final execution. · Evaluation & Testing Frameworks: Establish rigorous automated testing suits for agents to measure success rates, task completion accuracy, hallucination frequency, and tool invocation reliability. · Performance Optimization: Optimize application performance by implementing caching strategies, prompt compression, model routing, and asynchronous processing to reduce latency and token costs. Reliability, Safety & Governance (Agent-Specific) · Guardrails & Safety Layers: Implement advanced guardrail systems to prevent agents from taking unauthorized actions, accessing sensitive data, or entering infinite loops. This includes input/output filtering and constraint enforcement. · Deterministic Workflow Enforcement: Balance probabilistic LLM reasoning with deterministic workflow engines to ensure critical business processes remain predictable and auditable. · Observability & Debugging: Deploy comprehensive observability stacks specifically for agentic flows, tracing decision paths, tool calls, and reasoning steps to rapidly debug failures in complex autonomous chains. · Cost & Token Governance: Monitor and optimize token consumption and compute costs associated with high-frequency agent operations, implementing budget limits and efficiency protocols. Technical Leadership & Innovation · Framework Selection & Strategy: Evaluate and select the best open-source and proprietary agent frameworks and define the team’s technical stack. · POC to Production Pipeline: Rapidly prototype new agent concepts and define the clear criteria and engineering standards required to graduate them into mission-critical production applications. · Cross-Functional Integration: Collaborate closely with product managers and domain experts to translate vague business needs into structured agent specifications and actionable user stories. · Team Upskilling: Mentor engineers on prompt engineering patterns, chain-of-thought reasoning, few-shot learning, and the nuances of building non-deterministic software systems. ------------------------------------------- Principal Responsibilities Data Management and Extraction · Maintain an inventory of data assets, including databases, data sources, and formats, ensuring that all data is easily accessible and well-organized. · Design and implement processes for data extraction from various internal and external sources, ensuring accurate and timely retrieval of information. Data Integration · Collaborate with other functions to design and implement system integration solutions that ensure seamless data flow between various applications and platforms. · Manage and optimize databases to ensure efficient data storage, retrieval, and processing, supporting the organization’s data needs. Data Analytics and Tools · Collaborate with business units to gather data requirements, providing support for data analysis via reporting tools, system integration, and AI applications. · Work closely with various departments to understand their data needs and provide support for data-related projects and initiatives. · Oversee the selection, implementation, and management of data analytical tools and platforms (e.g., BI tools, data visualization software) to enhance data analysis capabilities across the organization. Agentic Architecture & System Design · Design Multi-Agent Systems: Architect scalable multi-agent orchestration frameworks where specialized agents collaborate to solve complex, multi-step business problems. · Build Advanced RAG Pipelines: Lead the development of next-gen Retrieval-Augmented Generation (RAG) systems that go beyond simple vector search, integrating Knowledge Graphs, hybrid search, and re-ranking models to provide agents with precise, context-aware grounding. · Memory & State Management: Design sophisticated long-term and short-term memory mechanisms (using vector DBs, SQL, or key-value stores) to allow agents to maintain context across long-running sessions and learn from past interactions. Application Engineering & Product Delivery · End-to-End Agent Lifecycle: Own the full software development lifecycle (SDLC) for AI applications, from prototype to production, ensuring agents are not just demos but reliable, latency-optimized products. · Human-in-the-Loop (HITL) Interfaces: Build intuitive UI/UX patterns for human-agent collaboration, including approval gates, intervention points, and feedback loops humans guide or correct agent actions before final execution. · Evaluation & Testing Frameworks: Establish rigorous automated testing suits for agents to measure success rates, task completion accuracy, hallucination frequency, and tool invocation reliability. · Performance Optimization: Optimize application performance by implementing caching strategies, prompt compression, model routing, and asynchronous processing to reduce latency and token costs. Reliability, Safety & Governance (Agent-Specific) · Guardrails & Safety Layers: Implement advanced guardrail systems to prevent agents from taking unauthorized actions, accessing sensitive data, or entering infinite loops. This includes input/output filtering and constraint enforcement. · Deterministic Workflow Enforcement: Balance probabilistic LLM reasoning with deterministic workflow engines to ensure critical business processes remain predictable and auditable. · Observability & Debugging: Deploy comprehensive observability stacks specifically for agentic flows, tracing decision paths, tool calls, and reasoning steps to rapidly debug failures in complex autonomous chains. · Cost & Token Governance: Monitor and optimize token consumption and compute costs associated with high-frequency agent operations, implementing budget limits and efficiency protocols. Technical Leadership & Innovation · Framework Selection & Strategy: Evaluate and select the best open-source and proprietary agent frameworks and define the team’s technical stack. · POC to Production Pipeline: Rapidly prototype new agent concepts and define the clear criteria and engineering standards required to graduate them into mission-critical production applications. · Cross-Functional Integration: Collaborate closely with product managers and domain experts to translate vague business needs into structured agent specifications and actionable user stories. · Team Upskilling: Mentor engineers on prompt engineering patterns, chain-of-thought reasoning, few-shot learning, and the nuances of building non-deterministic software systems.