REQUIRED QUALIFICATIONS:

• 8+ years of hands-on experience in software engineering, DevOps, or MLOps roles.
• Proven expertise working with advanced AI technologies, including LLMs, generative models, or real-time ML systems.
• Strong understanding of CI/CD pipelines, model versioning, and ML lifecycle management.
• Experience with tools such as MLflow, Kubeflow, Sage Maker, Airflow, Docker, Terraform, or Kubernetes.
• Solid programming skills in Python or Go, with emphasis on automation, reproducibility, and clean code.
• Familiarity with AWS ecosystem - EC2, S3, EKS, Sage Maker, Lambda, and IAM.
• Strong communication skills and the ability to translate complex technical requirements to various audiences.
  Job Type: Full-time
  Pay: $110,000.00 - $120,000.00 per year

Application Question(s):

• How many years of experience do you have in DevOps, or MLOps?
• Are you willing to work onsite in Alexandria, VA with client whenever needed? Travel cost need to be take care by the candidate
• Are you a U.S. Citizen?
• Do you have a Public Trust clearance or ability to obtain Public Trust?
• Provide valid Email ID? *MUST
• Expected Salary Range for Full-Time Opportunity?
• What is the best time to reach you?

Experience:

• LLM: 8 years (Preferred)
• AWS: 8 years (Preferred)
• Open AI : 8 years (Preferred)
• LangChain: 8 years (Preferred)

Work Location: In perso

KEY RESPONSIBILITIES:

• Design, develop, and maintain MLOps pipelines for training, deploying, monitoring, and managing ML models at scale.
• Collaborate closely with data scientists, machine learning engineers, and DevOps teams to implement reliable CI/CD processes for AI workloads.
• Automate and optimize ML workflows across multiple environments (development, staging, production).
• Ensure security, scalability, and compliance of AI solutions in accordance with federal standards.
• Integrate with AI frameworks such as Lang Chain, OpenAI, and other LLM tooling in cloud-native environments.
• Leverage cloud infrastructure (preferably AWS) to implement reproducible and containerized ML solutions (e.g., using Docker, SageMaker, Kubernetes).
• Monitor model performance in production using observability and drift detection techniques.
• Contribute to best practices, process automation, and documentation within a fast-paced AI-driven environment
• Provide technical mentorship to junior engineers and collaborate cross-functionally with stakeholders.