Data Science Team Lead at OREDATA, leading teams to resolve data science challenges in a hybrid environment. Joining a digital transformation firm with a strong collaborative culture.
About the role
- Lead AI projects focusing on researching, developing, and deploying semi-autonomous AI agents. Ensure business objectives are aligned while innovating on next-gen AI automation.
Responsibilities
- Architecting & Scaling Agentic AI Solutions • Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. • Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. • Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. • Hands-On Development & Optimization • Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. • Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. • Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. • Driving AI Innovation & Research • Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. • Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. • Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. • AI Strategy & Business Impact • Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. • Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. • Mentorship & Capability Building • Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. • Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents.
Requirements
- JD is below: The Agentic AI Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation. ________________________________________ Key Responsibilities 1. Architecting & Scaling Agentic AI Solutions • Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. • Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. • Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. 2. Hands-On Development & Optimization • Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. • Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. • Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. 3. Driving AI Innovation & Research • Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. • Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. • Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. 4. AI Strategy & Business Impact • Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. • Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. 5. Mentorship & Capability Building • Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. • Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Location requirements
Data Scientist analyzing claims data, optimizing processes and collaborating across departments at Allianz Spain. Utilizing statistical techniques and developing predictive models for operational efficiency.
Data Scientist enhancing merchandising and inventory performance using machine learning techniques at Nordstrom. Collaborating with teams to develop data - driven solutions for better customer experiences.
Clinical Data Manager supporting research projects and managing patient databases at Mass General Brigham. Interact with patients and maintain data integrity in clinical research.
Technical Lead in Data Science guiding analytical systems design and team mentorship for a finance technology company. Collaborating across engineering and research to drive innovation and data excellence.
Technical Lead in Data Science at Voleon, driving the design and implementation of analytical systems. Mentoring a growing team of data scientists while ensuring methodological rigor in finance applications.
Data Scientist developing analytical solutions for customer data transformation and supporting critical business decisions. Focus on quality and integrity of data through statistical models and analysis.
Junior Data Scientist developing demand forecasting models for Nestlé’s Supply Chain team. Collaborating with analysts and planners to improve forecasting tools and processes.
HCM Data and Process Analytics Lead at ABB building business intelligence for HCM solutions. Analyzing data to drive improvements in performance and data quality across the organization.
Data Scientist owning and delivering production - grade data pipelines at Simbe Robotics. Collaborating with Product Management, Engineering, and Commercial teams to surface insights from retail data.