Advisor Software Engineer (AI/ML)
$155,000–$209,000 year
On-siteReston, Virginia, United States
Reston, Virginia, United StatesOn-siteFull Time$155,000–$209,000 yearLarge
Full TimeLarge
Job Summary
Determine customer needs across multiple projects while resolving conflicting requirements, then design and develop scalable software solutions using a process-driven approach. Lead matrixed teams to coordinate simultaneous implementation tasks and oversee the maintenance of existing systems. Apply extensive expertise in Python, AWS cloud-native patterns, and AI/ML to build API-driven solutions, including GenAI applications and intelligent automation workflows. Collaborate with technical and business stakeholders to translate needs into technical solutions, manage risks, and deliver solutions aligned with enterprise standards.
Required Qualifications
- 6 years of hands-on software engineering experience designing, developing, and maintaining scalable enterprise applications and cloud-native solutions
- Strong proficiency in Python development, including backend services, APIs, automation, data processing workflows, and production-ready AI/ML applications
- Strong skills in system design and architecture, including scalable, resilient, secure, and maintainable solution design
- Experience building API-driven solutions, including REST APIs, microservices, service orchestration, secure API development, and enterprise system integrations
- Hands-on experience with AWS cloud-native development, including serverless, event-driven, containerized, and distributed application patterns
- Experience with SQL and data platforms, including PostgreSQL, Snowflake, or similar relational and analytical database technologies
- Deep understanding of the software development lifecycle, including requirements analysis, design, development, testing, deployment, production support, and maintenance
- Experience with engineering best practices, including secure coding, code reviews, automated testing, CI/CD, observability, performance tuning, and production issue resolution
- Experience collaborating with technical and business stakeholders, including translating business needs into technical solutions and communicating risks, trade-offs, and delivery impacts
- Master's Level Degree: Artificial Intelligence and Robotics
Desired Qualifications
- Bachelor's or master's degree in Computer Science, Engineering, Information Technology, Data Science, Machine Learning, Artificial Intelligence, or a related field
- Experience designing and delivering AI-enabled enterprise software solutions, including GenAI applications, intelligent automation, AI-assisted workflows, and AI-driven decision support
- Experience with MLOps, vector databases, embedding-based search, MCP-based tool integration, and enterprise AI governance practices
- Experience writing technical papers, invention disclosures, patent-supporting documentation, or reusable engineering playbooks for emerging technology solutions
- Experience with testing strategies and tools, including unit, integration, functional, regression, and performance testing
- Experience with Scaled Agile Framework, Agile methodology, cybersecurity vulnerability remediation, and enterprise delivery practices
- Strong relationship management skills with the ability to collaborate across stakeholders, influence outcomes, and support strategic enterprise technology initiatives
- Hands-on AWS software engineering experience, including application development using AWS service APIs, AWS CLI, AWS SDKs, and cloud-native deployment patterns
- Hands-on experience with core AWS services, including AWS Lambda, Amazon S3, Amazon EC2, Amazon API Gateway, IAM, CloudWatch, EventBridge, SQS, SNS, and Step Functions
- Experience with AWS AI/ML services, including Amazon SageMaker, Amazon Bedrock, and AWS-based model deployment or inference patterns
- Experience with containers and DevOps practices, including Docker, Kubernetes, ECS/EKS, CI/CD pipelines, automated testing, and release management
- Understanding of cloud security and compliance practices, including IAM, encryption, secrets management, vulnerability remediation, logging, and secure application design
- Hands-on experience in machine learning, AI engineering, data science, or applied AI solution development
- Hands-on experience with Generative AI and Large Language Models, including OpenAI, Anthropic, Cohere, Amazon Bedrock, or similar enterprise AI platforms
- Strong proficiency in Python and AI/ML libraries, including PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy, and related ML frameworks
- Experience building Retrieval-Augmented Generation solutions, including embeddings, vector databases, semantic search, document retrieval, chunking strategies, prompt grounding, and response evaluation
- Experience with LLM application patterns, including prompt engineering, guardrails, model evaluation, tool/function calling, agentic workflows, and responsible AI considerations
- Familiarity with AI application frameworks and tools, such as LangChain, LlamaIndex, FastAPI, MCP tools, vector databases, and API-based AI service integration
- Experience with model development and deployment practices, including feature engineering, model serving, model monitoring, MLOps, and productionizing AI/ML capabilities
- Proven experience leading technical delivery within software engineering teams, including solution direction, task assignment, progress monitoring, issue resolution, and delivery accountability
- Experience mentoring and coaching engineers, including technical guidance, code review feedback, design support, and professional development
- Ability to influence technical decisions and engineering practices, including architecture discussions, design trade-offs, quality improvements, and adoption of modern AI/ML and cloud engineering standards
- Experience partnering with product owners, architects, business stakeholders, risk, security, and operations teams to deliver solutions aligned with business outcomes and enterprise standards
- Ability to produce high-quality technical documentation, including architecture papers, solution design documents, technical white papers, AI/ML implementation guides, and executive-ready technical summaries
- Experience contributing to innovation artifacts, such as invention disclosures, patent-supporting technical writeups, proof-of-concept documentation, and publication-ready technical papers when applicable
- Proximity within a reasonable commute to your designated office location is preferred unless the job is noted as open to remote
- Amazon Web Services (AWS)
- Amazon Web Services (AWS)
- Atlassian JIRA
- AWS Machine Learning
- Business Process Management Skills
- Cloud Technology
- Communicating in Technical Writing
- Communication
- Computer Vision
- Configuration Management (CM)
- Coordination
- Customer and Market Insights
- Data Analysis Interpretation
- Data Mining
- Data Visualization
- Enterprise Information Security Architecture
- Gradient Boosting Algorithms
- Identity Management (IdM)
- Internal Auditing
- Knowledge Management
- Machine Learning (AI)
- Model Explainability
- Multi-modal Machine Learning Models
- Natural Language Processing (NLP)
- Neural Networks Methods and Algorithms
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