Agentic AI/LLM Engineer - Python/ETL

Job Description : Advanced Agentic AI/LLM Engineer

We are seeking an exceptionally skilled and innovative Advanced Agentic AI/LLM Engineer to lead the design and deployment of sophisticated, production-grade multi-agent and reasoning systems, specifically targeting complex tasks like credit analysis. This role requires a strong blend of LLM expertise, MLOps, and robust data engineering skills.

Experience & Qualifications :

- Experience : 5+ years of professional experience in Python or JavaScript development.

- Agentic AI Expertise : Proven, hands-on experience building and deploying production-grade Agentic AI or reasoning systems.

- LLM Ops : Strong background in LLM Operations (LLM Ops), data engineering, and managing AI workloads on major cloud platforms.

- Cloud Proficiency : Expertise with cloud platforms such as AWS or Azure.

- Technical Expertise : Deep expertise in prompt engineering, Retrieval-Augmented Generation (RAG), Human-in-the-Loop (HITL) processes, and multi-agent control architectures.

Key Roles & Responsibilities :

The Advanced Agentic AI/LLM Engineer will primarily focus on developing, deploying, and optimizing advanced AI systems :

1. Agentic Pipeline Design and Optimization :

- Design and deploy advanced multi-agent pipelines tailored for complex analytical tasks, initially focusing on sophisticated credit analysis.

- Optimize inference and prompt chains using cutting-edge frameworks like DSPy, GEPA, and LangChain to maximize system efficiency, cost-effectiveness, and output quality.

- Implement advanced reasoning techniques, including Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT), to enhance the decision-making capabilities of the AI agents.

2. System Evaluation and Reliability :

- Develop robust and rigorous evaluation systems (LLM-as-a-judge) to benchmark and continuously validate the performance and reliability of agentic outputs against human or ground-truth standards.

- Manage core infrastructure components, including memory, retrieval (vector stores, index management), and orchestration within complex multi-agent systems.

3. Data Engineering and MLOps Integration :

- Develop and maintain robust ETL workflows and data pipelines necessary to feed high-quality, relevant data to the multi-agent systems.

- Design and implement effective observability dashboards to monitor the health, performance, latency, and cost of all agentic and RAG components in real-time.

- Maintain and manage cloud-based AI services and seamlessly integrate them with existing DevOps pipelines for continuous deployment and infrastructure management.

Nice-to-Have Skills :

- Hands-on experience with advanced model alignment techniques such as Reinforcement Learning from Human Feedback (RLHF) or Reinforcement Learning from AI Feedback (RLAIF).

- Familiarity with efficient model fine-tuning methodologies, including LoRA/QLoRA.

- Experience or familiarity with graph data models (e.g., Neo4j) and graph neural networks (GNNs) for enhanced knowledge representation and retrieval.