AI/ML Engineer - Python &

SIDRAM TECHNOLOGIES; Reston (VA); On-site; USD 100,000 - 140,000; Full time

Job summary

A leading tech solutions firm in Virginia is seeking an AI/ML Engineer with strong skills in Python and AWS. In this full-time role, you will design and implement advanced AI/ML solutions, develop cloud-native applications, and build efficient microservices. The position requires expertise in various AWS services and the ability to create robust MLOps workflows. This is an onsite role requiring 5 days in the office, offering a dynamic environment for AI-driven projects.

Responsibilities

• Design and implement end-to-end AI/ML and Generative AI solutions using Python.
• Build and maintain cloud native applications on AWS.
• Develop high performance Python microservices enabling scalable data pipelines.
• Architect and operationalize RAG pipelines and LLM powered automation.
• Implement CI/CD pipelines and infrastructure as code.
• Build robust MLOps workflows including model versioning and monitoring.

Skills

AI/ML

Python

AWS

Generative AI

Fast API

Flask

Terraform

CloudFormation

CI/CD

MLOps

Tools

AWS Lambda

ECS

S3

DynamoDB

RDS/Aurora

SageMaker

GitHub

GitLab

CodePipeline

Job description

Overview

Role: AI/ML Engineer - Python & AWS

Duration: Fulltime/Contract

Onsite: 5 Days Onsite

Responsibilities

• Core Responsibilities (AI/ML, Python, AWS, GenAI)
• Design and implement end-to-end AI/ML and Generative AI solutions using Python, including model training, evaluation, optimization, and deployment.
• Build and maintain cloud native applications on AWS using services such as Lambda, ECS/Fargate, S3, API Gateway, DynamoDB, RDS/Aurora, SageMaker, and Bedrock.
• Develop high performance Python microservices (Fast API/Flask) enabling scalable data pipelines, model inference, and real time analytics.
• Architect and operationalize RAG pipelines, embeddings, vector databases, and LLM powered automation (chatbots, summarization, semantic search, anomaly detection).
• Implement CI/CD pipelines (GitHub/GitLab/CodePipeline) and infrastructure as code (Terraform/CloudFormation) for reliable, automated deployments.
• Build robust MLOps workflows, including model versioning, containerized training/inference, automated retraining, monitoring, and performance tuning.