Data Scientist - R01555533

11 Minutes ago • All levels
Data Analysis

Job Description

This pivotal Agentic AI Lead role drives the research, development, and deployment of semi-autonomous AI agents to tackle complex enterprise challenges. It requires hands-on experience with LangGraph to build multi-agent AI systems with enhanced autonomy and decision-making. Expertise in LLM orchestration, knowledge graphs, and reinforcement learning (RLHF/RLAIF) is essential. The role involves designing, scaling, and optimizing agentic AI workflows, integrating them with enterprise applications, and leading cutting-edge AI research to achieve business impact through automation and efficiency.
Must Have:
  • Design and develop multi-agent AI systems using LangGraph for workflow automation and autonomous problem-solving.
  • Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks.
  • Define and implement scalable architectures for LLM-powered agents that integrate with enterprise applications.
  • Develop and optimize agent orchestration workflows using LangGraph for high performance and scalability.
  • Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques.
  • Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making.
  • Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents.
  • Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings.

Add these skills to join the top 1% applicants for this job

team-management
forecasting-budgeting
data-structures
game-texts
prototyping
data-science
pytorch
reinforcement-learning
decision-trees
python
keras
tensorflow

Primary Skills

  • Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio

Specialization

  • Data Science Advanced: Data Specialist

Job requirements

  • JD: 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

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