Senior Cloud Software Engineer TS/SCI MLOps & AI in AWS

J&T Business Consulting; Laurel (MD); On-site; USD 218,000 - 259,000; Full time

Job summary

J&T Business Consulting in Laurel, MD is seeking a senior Cloud Software Engineer 3 to design and deploy ML-enabled cloud analytics prototypes in a secure, multi-tenant environment.

You will build scalable microservices with Docker/Kubernetes on AWS, integrate PostgreSQL/Neo4j/ClickHouse and related stores, and implement MLOps for production ML/LLM pipelines. Active TS/SCI with polygraph is required.

Qualifications

• Security Clearance: Active TS/SCI with a current Polygraph.
• ML & MLOps: Minimum 1 year of hands-on MLOps experience, including fine-tuning, evaluating, and operationalizing ML models and LLMs.
• Front-End & Containers: Hands-on experience with Angular or React, plus Docker containerization and Kubernetes orchestration.
• Cloud & OS: Working knowledge of AWS and experience across Linux (RHEL 8) and Windows environments.
• Databases: Experience developing against PostgreSQL, Neo4j, ClickHouse, Accumulo, MongoDB, SQLite, or DuckDB.

Responsibilities

• Design, build, and deploy multi-tenant cloud-based analytics prototypes.
• Implement, fine-tune, evaluate, and operationalize ML models and LLMs in production pipelines (MLOps).
• Build and manage containerized microservices with Docker and Kubernetes on AWS.
• Integrate diverse databases (PostgreSQL, Neo4j, ClickHouse, Accumulo, MongoDB, SQLite, DuckDB).
• Write automation scripts in Bash for RHEL 8 and Windows environments.
• Bridge statistics with ML in operational workflows.

Skills

ML & MLOps

Front-End Development

Cloud & DevOps

Data analysis

Education

Bachelor's degree in Computer Science/related

Associate degree + 2 years programming

5 years of programming experience in lieu of a degree

Tools

Docker

Kubernetes

AWS

RHEL 8 / Windows

PostgreSQL

Neo4j

ClickHouse

MongoDB

SQLite

DuckDB

Job description

J&T Business Consulting in Laurel, MD is seeking a senior Cloud Software Engineer 3 to design and deploy ML-enabled cloud analytics prototypes in a secure, multi-tenant environment.

You will build scalable microservices with Docker/Kubernetes on AWS, integrate PostgreSQL/Neo4j/ClickHouse and related stores, and implement MLOps for production ML/LLM pipelines. Active TS/SCI with polygraph is required.