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.