Senior Data Scientist - Agentic AI Lead
Job Summary
Job Description
The Agentic AI Lead will drive the research, development, and deployment of semi-autonomous AI agents to address complex enterprise challenges. This role requires hands-on experience with LangGraph and leadership in building multi-agent AI systems with enhanced autonomy and decision-making. Responsibilities include architecting and scaling agentic AI solutions using LangGraph, building memory-augmented, context-aware AI agents, and implementing scalable architectures for LLM-powered agents. The role also involves hands-on development and optimization of agent orchestration workflows, using knowledge graphs, vector databases, and reinforcement learning. Additionally, the lead will drive AI innovation through research in Agentic AI, LLM orchestration, and self-improving AI agents, and translate these capabilities into enterprise solutions to boost automation and efficiency. Mentorship of AI Engineers and Data Scientists is also a key aspect.
Must have:
- Experience with LangGraph
- Expertise in LLM orchestration
- Knowledge of knowledge graphs
- Experience with reinforcement learning (RLHF/RLAIF)
- Hands-on development of AI agents
- Experience with vector databases (Pinecone, Weaviate, FAISS)
- Proficiency in retrieval-augmented generation (RAG)
- Ability to lead AI research
- Skills in designing scalable AI architectures
- Mentoring AI engineering teams
Good to have:
- Problem Formulation
- Data Wrangling
- Data Storytelling
- Problem Solving
- Business Acumen
- Design Thinking
- Data Literacy
7 skills required
Job Details
Data Science Lead
- Problem Formulation (Business problem to Data Science Problem), OKR Validation against statistical measures, Data Wrangling, Data Storytelling & Insight Generation, Problem Solving, Excel VBA, Data Curiosity, Technical Decision Making (How many iterations to go for vs when to stop iterating), Communication & Articulation: Vocal & Written, Business Acumen (Consume new domains quickly to learn through data), Design Thinking, Data Literacy
- 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. ________________________________________
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