Lead the design and architecture of AI solutions. Develop scalable AI models and algorithms to address business needs. Identify opportunities for applying AI technologies to improve business processes. Work closely with data scientists, engineers, and stakeholders to integrate AI solutions. Coordinate with cross-functional teams to ensure successful deployment of AI projects. Provide technical guidance and mentorship to junior architects and team members. Review and approve design and code. Stay updated with the latest AI trends and technologies. Evaluate and implement new tools and frameworks to enhance AI capabilities. Ensure AI solutions comply with relevant laws and ethical standards. Implement security best practices in AI systems. Maintain comprehensive documentation of AI architectures and processes. Provide training and support to end-users and staff on AI applications.
Roles and Responsibilities
Collect and preprocess data for training models.
Develop and train machine learning models using appropriate algorithms and techniques.
Evaluate model performance using metrics such as accuracy, precision, recall, and F1 score.
Tune hyperparameters and adjust model architecture to enhance performance.
Deploy models to production environments, including cloud platforms and API endpoints.
Continuously monitor model performance and adjust to maintain accuracy and reliability.
Collaborate with cross-functional teams, including data scientists, data engineers, and software developers.
Stay informed about the latest advancements in machine learning and NLP research, applying them to improve model performance.
Work with Federal government customers in understanding agency missions and associated project objectives. This includes working with customers to gain an understanding of key pain points, project requirements, constraints, and potential RFP evaluation criteria.
Proven experience working as an Enterprise Machine Learning Architect or in a similar technical leadership role. Must have successfully implemented large-scale ML solutions in an enterprise environment, aligning with organizational goals.
Stay up to date with emerging technologies, industry trends, and best practices related to application development, cloud computing, data, cybersecurity, AI, and ML.
Demonstrated expertise in identifying high-impact business use cases for machine learning across various departmental functions such as data analytics, security, HR, finance, and more. Experience in designing and implementing scalable, innovative ML solutions that meet the strategic objectives of the organization.
Strong ability to assess and define the role of machine learning within the enterprise's broader goals. Must have an encompassing view of how ML can be applied across multiple domains to drive business value and efficiency.
Comprehensive understanding of how ML integrates with different enterprise systems and departments, including data platforms, security frameworks, human resources, finance, and more. Capable of working with cross-functional teams to deliver solutions that span the entire enterprise.
Proven ability to work with senior leadership and stakeholders to communicate the value of ML initiatives. Experience in driving organizational change through ML and AI-driven projects, ensuring alignment with business priorities.
Strong commitment to implementing Responsible AI principles, including fairness, accountability, transparency, and ethics in machine learning applications. Must have experience designing AI solutions that are compliant with regulatory standards and enterprise governance, while ensuring ethical use and minimization of bias in AI models.
Experience overseeing the entire ML lifecycle, from data preparation and model development to deployment and monitoring. Should have worked closely with Data Science, ML Ops, and DevOps teams to ensure operationalization, governance, and continuous monitoring of ML models.
Familiarity with productivity improvement tools such as Amazon Q for efficient data querying or Copilot for intelligent code suggestions, upgrades and automated documentation, enhancing coding efficiency and streamlining the development process for machine learning workflows.
Experience/Qualifications
Strong understanding of both supervised and unsupervised learning algorithms, as well as deep learning models.
Proficiency in Natural Language Processing (NLP), text mining, feature engineering, and data preprocessing techniques.
Proficient in programming languages such as Python, R, or Java.
Experience with machine learning frameworks such as TensorFlow, Keras, or PyTorch.
Experience with cloud platforms like AWS, Azure, or Google Cloud Platform.
Strong analytical and problem-solving skills.
Excellent communication abilities for effective collaboration and reporting.
Experience in Authority to Operate (ATO) preparation.
Minimum 8 years of experience in solution architecture in AI/ML systems, including data pipelines, model training, and data analytics.
Expertise in designing scalable, fault-tolerant, and high-performance machine learning architectures in cloud environments(preferred) such as AWS, Azure, or Google Cloud. Must have hands-on experience with managed ML services such as AWS SageMaker, Azure Machine Learning, or Google AI Platform (Machine Learning as a Service MLaaS).
Experience with Integration Platform as a Service (iPaaS) solutions for integrating machine learning models into enterprise applications
Proven experience with ML model quality control, including techniques for ensuring robustness, fairness, and accuracy. Expertise in setting up continuous model monitoring, drift detection, and alerting systems.
API Integration & External Interconnectivity: Strong experience in designing and deploying machine learning models that integrate with external enterprise systems via RESTful APIs or iPaaS platforms. Experience in securely connecting to external data sources and third-party services to enhance ML models and applications.
Generative AI (GenAI) & Large Language Models (LLM): Hands-on experience with Generative AI solutions and LLMs like Amazon Bedrock, OpenAI GPT, or similar. Ability to leverage these technologies to design, deploy, and fine-tune models for enterprise use cases, including chatbots, automated content generation, and knowledge management.
Agents & Agentic Code Interpreters: Expertise in building and deploying AI Agents capable of autonomously conducting tasks, running code, and making decisions based on context. Experience with Agentic Code Interpreters, such as those available through Amazon Bedrock or OpenAI Code Interpreter, to enable complex problem-solving and dynamic task execution.
Retrieval-Augmented Generation (RAG): Familiarity with RAG techniques to enhance LLMs by retrieving relevant knowledge from external sources (such as document databases or APIs) in real-time, improving the accuracy and relevance of AI outputs.
ML Frameworks & Libraries: Proficiency in popular machine learning frameworks and libraries.
Data Engineering & Pipeline Orchestration: Experience with building and managing data pipelines using Apache Airflow, Kubernetes, AWS Glue, or Azure Data Factory or similar. Strong knowledge of ETL (Extract, Transform, Load) processes and tools like Apache Spark, Databricks, and Snowflake.
Model Deployment & MLOps: Extensive knowledge of deploying machine learning models at scale using MLOps best practices. Familiarity with CI/CD pipelines for ML models. Experience with managing model versioning, retraining, and continuous monitoring in production.
Expertise in leveraging MLaaS platforms such as Google AI Hub, AWS SageMaker Studio, and Azure AI for rapid development, training, and deployment of machine learning models. Knowledge of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) for efficient infrastructure management and application hosting.
Strong understanding of data governance practices, security, and compliance within an enterprise environment. Experience with implementing data privacy and regulatory compliance in machine learning workflows. Experience with IAM (Identity and Access Management) policies and role-based access control for data and model security.
In-depth knowledge of Responsible AI principles, including model explainability, fairness, accountability, and bias detection. Must have experience implementing transparent and interpretable AI models with ethical considerations in mind.
Advanced knowledge of database management systems, including SQL (e.g., PostgreSQL, MySQL) and NoSQL (e.g., MongoDB, Cassandra, DynamoDB). Experience with data lakes and data warehouses, such as AWS Redshift, Google BigQuery, or Snowflake.
Proficiency in cloud infrastructure management and automation using tools like AWS CloudFormation, Azure Resource Manager, and Google Cloud Deployment Manager. Knowledge of infrastructure-as-code (IaC) and cloud orchestration tools such as Terraform and Ansible.
Ability to work with Data Scientists to translate business requirements into ML models. Familiarity with Python, R, and Jupyter Notebooks for collaborative development. Knowledge of ML Workflow Automation through MLaaS platforms to streamline the experimentation and deployment process.
Education and Certification(s)
Bachelor s degree in computer science, Information Management (IM), Information Technology, Engineering, Data Science, or equivalent with 8 years of technical experience, or 6 years experience in IT Solutions as a hands-on lead and architect (preferred)