AI/ML Lead Software Engineer

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.

HOW AN AI/ML SOFTWARE LEAD ENGINEER WILL MAKE AN IMPACT:

• 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.

WHAT YOU’LL NEED TO SUCCEED:

Required Education / Skills

• 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.

Preferred Skills

• 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.

Location: This is a hybrid role that requires 3 days per week at the client site in Southwest Washington DC.

Visa Sponsorship Will Not Be Provided for This Position

Clearance: Candidates must be eligible to obtain a federal security clearance

GDIT IS YOUR PLACE:

• Full-flex work week to own your priorities at work and at home

• 401K with company match

• Comprehensive health and wellness packages

• Internal mobility team dedicated to helping you own your career

• Professional growth opportunities including paid education and certifications

• Cutting-edge technology you can learn from

• Rest and recharge with paid vacation and holidays