AI/ML Engineer

Robotics Technologies LLC

Malvern, PA, United States

about 2 months ago

Role details

Contract type

Temporary contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

6 years minimum

Working hours

Regular working hours

Job source

indeed.com

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Continuous Integration Data Dictionary Information Engineering Python (Programming Language) Machine Learning Tensorflow Azure Machine Learning Software Engineering Workflow Management Systems

+18 more

Cloud Platform System Okta Pytorch ReactJS Large Language Models Generative AI AWS Lambda Git Containerization Scikit Learn Kubernetes Information Technology AWS Glue AWS Data Analytics Machine Learning Operations Software Version Control Data Pipelines Docker

Job description

Agentic AI & MCP Integration: Implement agentic frameworks (e.g., LangGraph, AutoGen) and Model Context Protocol (MCP) for secure tool orchestration.
Generative AI Development: Build LLM-based applications with RAG, structured output, and evaluation frameworks.
AWS ML Engineering: Deploy models using SageMaker pipelines, ECS/ECR, Lambda; manage CI/CD and monitoring.
Security & Identity: Integrate Okta/JWT token for API and service authentication; enforce token validation and claims.
Governance : Deliver artifacts required by MDLC/MPLC (Model Documents, Data Dictionary, Monitoring Plan).
Collaboration: Partner with PO, and business stakeholders to align solutions with objectives.

Requirements

Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).
6+ years of experience across Artificial Intelligence (AI) / Machine Learning (ML) engineering, data engineering, and MLOps implementation, including:
Designing and deploying production-grade ML systems.
Building scalable data pipelines and ML workflows.
Managing model lifecycle in cloud environments.
Proficient in Python and familiar with ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
Strong understanding and experience in AWS Machine Learning Stack including:
AWS SageMaker
AWS Glue
AWS Bedrock
AWS Data Pipelines
AWS Lambda Functions
Experience with Generative AI model development builing LLM based applications with RAG.
Experience implementing agentic frameworks (e.g., LangGraph, AutoGen) and Model Context Protocol (MCP) for orchestration.
Knowledge of React UI, GraphDB, and GenAI model performance evaluation
Experience with CI/CD, containerization (e.g., Docker), and orchestration tools (e.g., Kubernetes).
Solid grasp of software engineering principles including testing, version control (e.g., Git), and security.
Familiarity with the Machine Learning Development Lifecycle (MDLC) and best practices for reproducibility and scalability.
Strong communication and collaboration skills, with experience working across technical and business teams.
Ability to anticipate ambiguity and devise scalable solutions to address it.