SCI with Poly Required)

GCI Incorporated•Chantilly, VA

6d•$124,093 - $207,251

About The Position

GCI embodies excellence, integrity and professionalism. The employees supporting our customers deliver unique, high-value mission solutions while effectively leverage the technological expertise of our valued workforce to meet critical mission requirements in the areas of Data Analytics and Software Development, Engineering, Targeting and Analysis, Operations, Training, and Cyber Operations. We maximize opportunities for success by building and maintaining trusted and reliable partnerships with our customers and industry. At GCI, we solve the hard problems. As an AI/ML Software Engineer, a typical day will include the following duties: The AI/ML Software Engineer will design, develop, deploy, and maintain advanced artificial intelligence and machine learning solutions in mission-critical environments. The ideal candidate is a hands-on engineer with experience building scalable AI-powered applications and machine learning pipelines using cloud-native services. This role requires expertise in integrating, deploying, and optimizing machine learning models, large language models (LLMs), retrieval-augmented generation (RAG) systems, and data processing frameworks within secure cloud environments. The successful candidate will possess strong software engineering fundamentals combined with practical experience in AI/ML development, MLOps, cloud infrastructure, and data engineering. They must be comfortable working within an Agile, cross-functional team and demonstrate a passion for innovation, continuous learning, and operational excellence.

Requirements

Bachelor's degree in Computer Science, Information Technology, or other related technical discipline, or equivalent combination of education, technical certifications, training, and work/military experience.
Demonstrated hands-on experience with Python and modern software engineering practices, including Git, automated testing, and code reviews.
Demonstrated hands-on experience developing and deploying RESTful APIs and microservices.
Demonstrated experience building, integrating, and deploying machine learning models in production environments.
Demonstrated experience with generative AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent technologies.
Demonstrated experience working with Large Language Models (LLMs), prompt engineering, model evaluation, and retrieval-augmented generation (RAG) architectures.
Demonstrated hands-on experience with vector databases and semantic search technologies, including OpenSearch, Elasticsearch, Pinecone, Weaviate, Chroma, or equivalent platforms.
Demonstrated hands-on experience with AWS cloud services and AI/ML offerings, including S3, EC2, IAM, VPC, SageMaker, Bedrock, Lambda, and related services.
Demonstrated experience applying object-oriented design principles and software architecture patterns to build scalable, maintainable, and secure production systems.
Demonstrated experience designing and implementing data pipelines supporting machine learning training and inference workloads.
Understanding of MLOps principles, including model versioning, deployment automation, monitoring, and lifecycle management.
A candidate must be a US Citizen and requires an active/current TS/SCI with Polygraph clearance.

Nice To Haves

Demonstrated hands-on experience with AWS Bedrock, SageMaker, Amazon OpenSearch Service, or equivalent cloud AI platforms.
Demonstrated hands-on experience with Infrastructure as Code tools such as AWS CDK v2, Terraform, or CloudFormation.
Demonstrated experience fine-tuning, evaluating, or optimizing foundation models and open-source LLMs.
Demonstrated experience deploying containerized AI workloads using Docker and Kubernetes.
Demonstrated experience building event-driven and serverless AI architectures using AWS Lambda, API Gateway, SNS, SQS, EventBridge, or Step Functions.
Demonstrated experience implementing AI/ML data pipelines using AWS Glue, Athena, EMR, Spark, or equivalent technologies.
Demonstrated experience with vector embeddings, semantic search, knowledge graphs, and enterprise search platforms.
Demonstrated experience with orchestration platforms such as Airflow, Dagster, Kubeflow, MLflow, or Prefect.
Demonstrated experience implementing MLOps pipelines for model training, validation, deployment, and monitoring.
Demonstrated experience with feature stores, model registries, and experiment tracking platforms.
Demonstrated experience working with DynamoDB, PostgreSQL, RDS, Hive, or NoSQL data platforms.
Demonstrated experience with Parquet, ORC, Delta Lake, or Iceberg data formats and architectures.
Demonstrated experience optimizing cloud infrastructure costs and AI workload performance.
Demonstrated hands-on experience with Linux-based systems, shell scripting, and cloud-native operations.
Experience implementing Responsible AI, model governance, security controls, and AI risk management frameworks.
Experience working within government, defense, intelligence, or other highly regulated mission environments.

Responsibilities

Design, develop, test, debug, and deploy AI-enabled software applications, machine learning services, and intelligent automation tools.
Develop and maintain scalable cloud-native and on-prem AI/ML solutions supporting mission-critical operations.
Build and integrate machine learning models, generative AI capabilities, and LLM-powered applications into production systems.
Design and implement Retrieval-Augmented Generation (RAG) architectures leveraging vector databases, embeddings, and enterprise knowledge repositories.
Develop and maintain data ingestion, transformation, feature engineering, and model inference pipelines.
Collaborate with data scientists, machine learning engineers, analysts, project managers, and subject matter experts to operationalize AI capabilities.
Deploy AI/ML workloads within AWS-based cloud environments using Infrastructure as Code (IaC) and automated CI/CD pipelines.
Design and optimize vector search, semantic search, and traditional search solutions using OpenSearch, Elasticsearch, or equivalent technologies.
Implement model monitoring, observability, performance tuning, and automated retraining workflows.
Ensure responsible AI practices, including model 'explainability', governance, security, privacy, and compliance requirements.
Troubleshoot complex production issues involving AI models, data pipelines, cloud services, and distributed systems.
Maintain technical documentation for AI architectures, model deployment processes, and operational procedures.
Research and evaluate emerging AI, machine learning, and cloud technologies and provide recommendations for continuous improvement.
Partner with engineering teams to advance organizational AI capabilities and accelerate adoption of modern AI technologies.