Senior Data Scientist (AWS Bedrock, Agentic AI, LLMs , Databricks )

Lester Infoservices; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Lester Infoservices is seeking an experienced Senior Data Scientist to lead Agentic AI initiatives. You will design and deploy multi-agent AI systems, work with LangGraph and LangChain, and build scalable ML/AI workflows across data pipelines and production environments.

Ideal candidates have hands-on experience with MCP, RAG pipelines, Databricks, AWS Bedrock, and end-to-end MLOps, delivering production-grade GenAI solutions in a fully remote setting.

Qualifications

• 6–8 years in Data Science, ML, or AI
• 3+ years in Generative AI/LLM-based apps
• Strong Python and SQL skills
• Hands-on with LangGraph, LangChain, and multi-agent AI architectures
• Experience with MCP (Model Context Protocol)
• Knowledge of RAG, embeddings, vector databases and LLM evaluation
• Experience with Databricks, PySpark, Delta Lake, MLflow and Unity Catalog
• Experience with AWS Bedrock, including Agents, Knowledge Bases and Guardrails
• Understanding of LoRA/QLoRA/PEFT and LLM fine-tuning
• Strong foundation in traditional ML, modelling, and evaluation
• Experience with MLOps, CI/CD, deployment and monitoring

Responsibilities

• Design, develop and deploy Agentic AI and multi-agent apps using LangGraph or LangChain.
• Build autonomous AI workflows with tool calling, orchestration, memory and human-in-the-loop features.
• Develop and integrate MCP servers/clients for AI agents with external tools and APIs.
• Design and optimize RAG pipelines with embeddings, vector databases and retrieval.
• Work with AWS Bedrock Agents, Knowledge Bases and Guardrails for GenAI solutions.
• Develop data/AI workflows using Databricks, PySpark, Delta Lake, MLflow and Unity Catalog.
• Implement Databricks Vector Search, Model Serving, Apps and Workflows for production systems.
• Perform LLM evaluation, prompt engineering and observability for performance.
• Explore LoRA/QLoRA/PEFT and other fine-tuning techniques.
• Build traditional ML/DL models and end-to-end MLOps pipelines.
• Collaborate with stakeholders to translate business needs into scalable AI solutions.

Skills

Python

SQL

LangGraph

LangChain

Multi-Agent AI

MCP

Databricks

AWS Bedrock

LLM evaluation

MLOps

Tools

Databricks

AWS Bedrock

LangGraph

LangChain

Job description

Job Title: Senior Data Scientist Agentic AI

Experience: 6 to 8 Years
Location: 100% Remote
Employment Type: Full-Time

Job Summary

We are looking for an experienced Senior Data Scientist Agentic AI with strong expertise in Python, Machine Learning, Generative AI, LLMs and Multi-Agent AI systems.

The ideal candidate should have hands-on experience in designing, developing and deploying production-grade Agentic AI applications, RAG pipelines and intelligent automation solutions. The candidate should be proficient in LangGraph, LangChain, MCP, Databricks and AWS Bedrock, with a strong understanding of traditional Machine Learning, model development and MLOps.

Key Responsibilities

• Design, develop and deploy production-grade Agentic AI and multi-agent applications using LangGraph, LangChain or similar frameworks.
• Build autonomous AI workflows with tool calling, agent orchestration, memory management, state management and human-in-the-loop capabilities.
• Develop and implement MCP servers and clients to integrate AI agents with external tools, APIs and enterprise applications.
• Design and optimise RAG pipelines using embeddings, vector databases, semantic search, reranking and document processing.
• Work with AWS Bedrock Agents, Knowledge Bases and Guardrails to build secure and scalable GenAI solutions.
• Develop and manage data and AI workflows using Databricks, PySpark, Delta Lake, MLflow and Unity Catalog.
• Implement Databricks Vector Search, Model Serving, Apps and Workflows for production AI applications.
• Perform LLM evaluation, prompt engineering, hallucination detection, observability and performance optimisation.
• Work on LLM fine-tuning techniques such as LoRA, QLoRA and PEFT.
• Develop traditional Machine Learning and Deep Learning models, including classification, regression, forecasting and NLP solutions.
• Implement end-to-end MLOps pipelines covering model training, validation, deployment, monitoring and retraining.
• Collaborate with business stakeholders, data engineers and engineering teams to translate business requirements into scalable AI solutions.

Required Skills & Experience

• 6 to 8 years of overall experience in Data Science, Machine Learning, AI or related domains.
• Minimum 3 years of hands-on experience in Generative AI and LLM-based applications.
• Strong programming experience in Python and SQL.
• Hands-on experience with LangGraph, LangChain and Multi-Agent AI architectures.
• Practical experience with MCP (Model Context Protocol).
• Strong knowledge of RAG, embeddings, vector databases and LLM evaluation.
• Hands-on experience with Databricks, PySpark, Delta Lake, MLflow and Unity Catalog.
• Experience with AWS Bedrock, including Agents, Knowledge Bases and Guardrails.
• Understanding of LoRA, QLoRA, PEFT and LLM fine-tuning.
• Strong foundation in traditional Machine Learning, statistical modelling and model evaluation.
• Experience with MLOps, CI/CD, model deployment and production monitoring.

Good to Have

• Experience in manufacturing, automotive, industrial or supply chain domains.
• Exposure to Computer Vision, NLP and Deep Learning.
• Experience with Docker, Kubernetes, FastAPI and REST APIs.
• Knowledge of AI governance, security, responsible AI and enterprise-scale deployments.

Mandatory Technical Skills

Python | LangGraph | MCP | Databricks | AWS Bedrock

Important:

Candidates must have hands-on experience building and deploying production-grade Agentic AI solutions, not just theoretical knowledge or certification exposure.