At GDIT, we deliver clarity with cloud, AI, and data-driven solutions that modernize mission‑critical systems for federal clients. We are seeking an experienced AI/ML Lead Software Engineer to design, develop, and deploy advanced machine learning and AI solutions. You will partner with data scientists and engineers to build scalable systems, integrate AI into enterprise platforms, and deliver production-ready capabilities.

How You’ll Make an Impact

• Design and implement machine learning models for use cases including predictive analytics, NLP, computer vision, and recommendation systems.
• Collect, clean, and preprocess large structured and unstructured datasets; ensure data quality and relevance.
• Train, validate, and optimize models using modern frameworks and best practices.
• Engineer features and apply domain knowledge to improve model accuracy and generalization.
• Deploy models to production and integrate them with enterprise applications, APIs, and MLOps workflows.
• Build scalable solutions for batch or real-time inference; package, version, and automate deployments with CI/CD.
• Monitor production models for drift, performance, reliability, and trigger retraining as needed.
• Document technical designs, data lineage, assumptions, and best practices.
• Collaborate with cross-functional teams to understand requirements and deliver impactful AI capabilities.
• Provide guidance to junior team members and serve as a task or team lead when needed.
• Work independently with general supervision.

Required Education / Skills

• Bachelor’s degree and 8+ years of experience.
• Hands-on experience with Alteryx.
• Strong Python skills (Pandas, NumPy, scikit-learn) and SQL expertise.
• Solid understanding of statistics (probability, hypothesis testing, regression, A/B testing).
• Experience across the full ML lifecycle: feature engineering, training, evaluation, deployment, and monitoring.
• Data wrangling and pipeline development (ETL/ELT) for large and complex datasets.
• Model evaluation (metrics selection, bias/variance, error analysis).
• MLOps integration and experience with API-based AI services.
• Production deployment experience including packaging, versioning, CI/CD, and monitoring (drift, performance).
• Experience with at least one major cloud platform (Azure, AWS, or GCP).
• Familiarity with Docker and Git.
• Data visualization skills (Power BI or Tableau).
• Strong system analysis abilities to identify AI use cases.
• Clear communication of technical concepts and business value.
• Knowledge of Responsible AI principles and data security practices.
• Ability to support 24x7 environments when required.

Preferred Skills

• Experience with large language models (Azure OpenAI, OpenAI API), prompt engineering, and LLM quality/safety evaluation.
• Experience with RAG pipelines and vector databases (Azure AI Search, Pinecone, FAISS).
• Knowledge of LLM fine‑tuning and domain adaptation.
• Experience with experiment tracking/orchestration tools (MLflow, Weights & Biases).
• Kubernetes and ML deployment tools (AKS, EKS, Argo, KServe).
• Feature stores, A/B testing frameworks, and streaming platforms (Kafka, Kinesis).
• CI/CD and IaC tools (GitHub Actions, Azure DevOps, Terraform, Bicep).
• Experience with Databricks, Snowflake, or BigQuery.
• Strong API development (REST/GraphQL) for ML workloads.
• Monitoring/observability tools (Prometheus, Grafana).
• Responsible AI tools (SHAP, LIME) and model risk management.
• Knowledge of privacy-by-design, PII handling, and regulated environments (e.g., FedRAMP).
• Experience with R, PySpark, or Scala for large-scale data workloads.
• Experience with LangChain or Semantic Kernel for LLM applications.
• Advanced Power BI or Tableau (parameterized dashboards, row-level security).