Data Scientist - Agentic AI Developer

Overview

Data Scientist - Agentic AI Developer role to develop, deploy, and monitor production-ready machine learning, Generative AI, LLM, RAG, and Agentic AI solutions. Full-time and remote.

What you'll do

• Partner with business and technical stakeholders to define problems, success metrics, KPIs, constraints, and solution approaches
• Perform data sourcing, exploratory data analysis, statistical analysis, feature engineering, data-quality checks, and hypothesis testing
• Build and evaluate machine learning, deep learning, time-series, anomaly-detection, recommendation, and optimization models
• Develop production-ready LLM, RAG, and Agentic AI applications using LangGraph, LangChain, Amazon Bedrock Agents, or similar frameworks
• Design multi-step and multi-agent workflows involving tool calling, state and memory management, human-in-the-loop approvals, retries, fallbacks, tracing, and MCP integrations
• Build secure and scalable AI solutions using Databricks Apps, MLflow, Model Serving, Vector Search, Workflows, Delta Lake, and Unity Catalog
• Develop applications on Amazon Bedrock using foundation models, Knowledge Bases, Agents, Guardrails, prompt management, and model evaluation
• Design advanced RAG pipelines involving hybrid retrieval, metadata filtering, re-ranking, query rewriting, embeddings, vector databases, and retrieval evaluation
• Build NLP solutions for text classification, named entity recognition, information extraction, semantic search, and document summarization
• Develop computer vision solutions for classification, object detection, segmentation, OCR, and visual defect inspection
• Fine-tune LLMs, embedding models, and vision models using LoRA, QLoRA, PEFT, and Hugging Face frameworks
• Develop and deploy AI services using FastAPI, REST APIs, Docker, Lambda, API Gateway, ECS, EKS, and CI/CD pipelines
• Monitor model performance, drift, quality, latency, reliability, and cost
• Conduct error analysis, model evaluation, and continuous improvement based on technical and business metrics
• Communicate technical findings, trade-offs, risks, and recommendations to business and engineering stakeholders

What you'll need

• 6 to 8 years of Data Science experience, including at least 3 years of hands-on Generative AI experience
• At least 5 years of advanced Python development experience
• Strong expertise in Python, Pandas, NumPy, PySpark, SQL, and object-oriented programming
• Strong foundations in statistics, probability, machine learning, deep learning, hypothesis testing, and model evaluation
• Hands-on experience with Scikit-learn, XGBoost, LightGBM, and PyTorch or TensorFlow
• Experience building models and AI applications from first principles rather than relying only on AutoML or pre-built components
• Hands-on experience building LLM applications using Claude through Amazon Bedrock or other foundation models
• Strong understanding of prompt engineering, structured outputs, function and tool calling, context management, caching, and LLM evaluation
• Proven experience building at least one end-to-end multi-step or multi-agent system
• Experience with agent tool design, state and memory management, human-in-the-loop workflows, retries, fallbacks, evaluation, tracing, and MCP
• Experience with RAG, embeddings, vector databases, hybrid search, metadata filtering, re-ranking, and query rewriting
• Knowledge of retrieval evaluation metrics including recall@k, MRR, and nDCG
• Hands-on experience with Databricks Apps, MLflow, Model Serving, Vector Search, Workflows, Delta Lake, and Unity Catalog
• Hands-on experience with AWS Bedrock, S3, IAM, Lambda, API Gateway, OpenSearch, SageMaker, and CloudWatch
• Experience with FastAPI, REST APIs, Git, Docker, automated testing, CI/CD, and Infrastructure as Code
• Experience deploying and monitoring production-ready machine learning and Generative AI solutions
• Strong stakeholder communication skills and the ability to explain technical trade-offs to business and engineering teams

Nice to have

• Experience developing NLP solutions including text classification, named entity recognition, information extraction, semantic search, and document summarization
• Experience developing computer vision solutions including classification, object detection, segmentation, OCR, and visual defect inspection
• Experience fine-tuning LLMs, embedding models, or vision models using LoRA, QLoRA, PEFT, or Hugging Face
• Experience deploying containerized AI services using ECS or EKS
• Experience working on enterprise-scale AI solutions in manufacturing, industrial, engineering, or supply chain environments

Details

• Full-time, remote
• Physical demands: sedentary work involving sitting most of the time, with occasional need to move around the office for meetings; repetitive computer tasks using mouse, keyboard, and monitor