Data Scientist - Agentic Developer

What You’ll Do

• Design, develop, and deploy multi-agent systems and agentic applications using frameworks like AutoGen, LangGraph, CrewAI, or similar

• Build intelligent workflow orchestration systems that enable autonomous decision-making and task execution

• Implement Agent-to-Agent (A2A) communication protocols and Model Context Protocol (MCP) for seamless agent collaboration

• Develop automation solutions using OpenAPI standards for integration with enterprise systems

• Create self-healing, adaptive workflows that optimize business processes autonomously

• Use ML, deep learning, and Generative AI tools to design, evangelize, and implement state-of-the-art solutions

• Define and implement best practices for building, testing, and deploying scalable AI solutions, with a focus on generative models and LLMs using proprietary or open-source models

• Drive successful business outcomes by designing and building cloud-hosted Generative AI solutions

• Work closely with internal teams to integrate RAG workflows, agent-based systems, and automation frameworks into applications

• Design and implement architectural solutions for Information Retrieval using RAG, Vector DBs, and Knowledge Graphs

• Work with public cloud (AWS) and on-premises infrastructure for deploying LLMs, agents, and orchestration systems

• Evaluate, build, and fine-tune ML models and LLMs to solve complex business problems

• Stay abreast of latest developments in agentic AI, autonomous systems, language models, and generative AI technologies.

What You'll Need

Required:

• BE, Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or equivalent practical experience

• 4-8 years of overall technical experience with 0-2 years of hands-on experience in Generative AI and LLM technologies

• 1+ years of experience building agentic systems, workflow automation, or autonomous AI applications

• Deep hands-on experience with agentic frameworks (AutoGen, LangGraph, CrewAI, Agency Swarm, or similar)

• Strong knowledge of workflow orchestration tools and patterns (Temporal, Airflow, Prefect, or similar)

• Expertise in OpenAPI standards, Agent-to-Agent (A2A) protocols, and Model Context Protocol (MCP)

• Experience designing multi-agent architectures with memory, planning, and tool-use capabilities

• Knowledge of agent evaluation, testing frameworks, and observability patterns

• Proven track record of deploying and optimizing LLM models for inference in production environments

• Extensive experience with LLM orchestration frameworks (LangChain, LlamaIndex required)

• Hands-on experience with Amazon Bedrock, SageMaker JumpStart, and other cloud-based LLM platforms

• Expertise in RAG architectures, Fine-tuning techniques, and Prompt Engineering

• Deep understanding of Vector Databases (Pinecone, Weaviate, Milvus, ChromaDB) and Knowledge Graphs

• Expert in NLP techniques and deep learning libraries (Transformer models, LSTM, BiLSTM, CNN, BERT, GPT, T5)

• Proficiency with ML frameworks: TensorFlow, PyTorch, Hugging Face Transformers, scikit-learn

• Strong programming skills in Python (required), plus JavaScript/TypeScript or Node.js

• Deep understanding of data structures, algorithms, and system design patterns

• Hands-on experience in MLOps/LLMOps including data pipelines, model training/refinement, validation, drift management, and serving

• Experience with containerization (Docker, Kubernetes) and CI/CD pipelines for ML systems

• Knowledge of monitoring, logging, and observability tools for production AI systems.

Preferred:

• Experience with function calling, tool use, and external API integration in agent systems

• Knowledge of reinforcement learning and agent training methodologies

• Familiarity with semantic reasoning, planning algorithms (ReAct, Chain-of-Thought, Tree-of-Thoughts)

• Experience with graph databases (Neo4j, Neptune) and ontology design

• Contributions to open-source AI/ML projects

• Publications or patents in AI/ML domain

Physical Demands

• Ability to safely and successfully perform the essential job functions

• Sedentary work that involves sitting or remaining stationary most of the time with occasional need to move around the office to attend meetings, etc

• Ability to conduct repetitive tasks on a computer, utilizing a mouse, keyboard, and monitor