JOB DESCRIPTIONThe 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.
KEY 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.
EDUCATION AND EXPERIENCE- 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.
REQUIRED QUALIFICATIONS- 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.
DESIRED QUALIFICATIONS- 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.
*A candidate must be a US Citizen and requires an active/current TS/SCI with Polygraph clearance. 
