AI/ML Lead Software Engineer

General Dynamics Information Technology•Washington, DC

1d•Hybrid

About The Position

At GDIT, we deliver clarity with our cloud, AI, and data-driven solutions—and we provide work that makes a real impact. Your expertise will help modernize mission-critical systems and accelerate innovation for our federal clients. We are seeking an experienced AI/ML Lead Software Engineer responsible for designing, developing, and implementing advanced machine learning models and artificial intelligence solutions to solve complex problems, optimize processes, and enhance decision-making. This role works closely with data scientists and software engineers to build scalable, efficient systems powered by advanced algorithms and large datasets. If you excel at architecting AI/ML solutions, integrating with enterprise platforms, and delivering production-ready models, this role offers the opportunity to drive significant technical impact.

Requirements

Bachelor’s degree in a relevant field and 8+ years of experience.
Hands-on experience with the Alteryx data blending platform.
Strong Python skills including data manipulation, model development, and experience with libraries such as Pandas, NumPy, and scikit-learn.
SQL proficiency including joins, window functions, and performance-optimized queries.
Knowledge of statistical foundations such as probability, hypothesis testing, regression, and experimental design or A/B testing.
End-to-end machine learning workflow experience including feature engineering, training, validation, deployment, and monitoring.
Experience with data wrangling, ETL and ELT, and building reliable data pipelines capable of handling large and messy datasets.
Experience with model evaluation including metrics selection, bias and variance analysis, and error analysis.
Ability to integrate AI solutions with MLOps workflows.
Experience integrating APIs for AI services such as model endpoints and microservices.
Experience deploying models in production environments including packaging, versioning, and CI/CD for machine learning.
Experience monitoring deployed models including drift detection, performance tracking, and setting retraining triggers.
Experience with at least one major cloud platform such as Azure, AWS, or GCP for data and AI workloads.
Familiarity with Docker and Git.
Skill in data visualization using Power BI or Tableau.
Strong system analysis skills to identify viable AI insertion points in business processes, products, or workflows.
Ability to clearly communicate technical findings and translate them into business value.
Ability to document models, assumptions, data lineage, and decisions.
Awareness of Responsible AI principles including fairness, explainability, privacy, and compliance considerations.
Basic understanding of data security practices and access controls in production environments.

Nice To Haves

Experience with large language models such as Azure OpenAI Service or the OpenAI API for summarization, classification, or copilots.
Experience with prompt engineering and evaluating LLM outputs for quality and safety.
Experience with RAG pipelines and vector databases such as Azure AI Search, Pinecone, or FAISS.
Knowledge of fine‑tuning and adaptation strategies for domain-specific use cases.
Experience with model orchestration and experiment tracking tools such as MLflow or Weights & Biases.
Experience with Kubernetes and ML deployment tools such as AKS, EKS, Argo, or KServe.
Experience with feature stores, A/B testing frameworks, and event-driven or streaming services such as Kafka or Kinesis.
Experience with CI/CD tools such as GitHub Actions or Azure DevOps and with IaC tools such as Terraform or Bicep.
Experience with Databricks, Snowflake, or BigQuery.
Experience building robust APIs such as REST or GraphQL and microservices supporting machine learning workloads.
Knowledge of monitoring and observability technologies such as Prometheus, Grafana, and associated logs.
Experience with Responsible AI and compliance practices including explainability tools such as SHAP and LIME and model risk management.
Knowledge of privacy-by-design and PII handling practices including minimization and anonymization.
Familiarity with FedRAMP or other regulated environments if applicable.
Experience using R, PySpark, or Scala for large-scale data workloads.
Experience with LangChain or Semantic Kernel for developing LLM applications.
Advanced Tableau or Power BI experience including parameterized dashboards and row-level security.
Ability to support a 24x7 environment for business-critical or SLA-driven workloads.

Responsibilities

Design, develop, implement, and use machine learning algorithms and models to address business challenges and opportunities such as predictive analytics, natural language processing, computer vision, and recommendation systems.
Collect, clean, and preprocess large volumes of structured and unstructured data from various sources, ensuring data quality, integrity, and relevance for model training and evaluation.
Train, validate, and optimize machine learning models using state-of-the-art techniques and frameworks.
Evaluate model performance, interpret results, and iterate on model design as needed.
Extract, select, and engineer relevant features from raw data to improve model performance and generalization capabilities.
Utilize domain knowledge and data exploration techniques to identify informative features.
Deploy machine learning models into production environments and integrate them with existing systems and applications.
Implement scalable, efficient, and reliable solutions for real-time or batch inference.
Monitor model performance, reliability, and scalability in production environments.
Implement automated monitoring and alerting systems to detect anomalies and performance degradation.
Document technical designs, implementation details, and best practices for AI solutions.
Collaborate with cross-functional teams including data scientists, software engineers, product managers, and stakeholders to understand requirements, prioritize projects, and deliver impactful AI solutions.
Perform additional duties as assigned.
Coach and provide guidance to less experienced professionals as required.
Serve as a team or task lead if needed.
Work independently under general supervision.