Senior AI Software Engineer

Sr. AI Software Engineer

Essential Software Inc. (ESI)

Essential Software Inc. is a trusted partner delivering innovative technology solutions that advance critical missions. Our teams provide direct support to the National Cancer Institute’s (NCI) large-scale data and research initiatives, providing secure, cloud-based platforms for scientific discovery in direct support of client mission.

We are seeking an AI Senior Software Engineer to innovate, design, build, and ship production grade GenAI features. Join an agile team that experiments, prototypes, and delivers solutions that make complex data and tools more accessible to scientists who are working to save lives.

• Take end to end ownership of features from design and estimation through implementation, testing, deployment, and production support.
• Translate research and product requirements from scientific stakeholders into concrete technical designs and working software.
• Partner with product owners, architects, and applied scientists to prototype and validate new AI driven capabilities, then harden them for production use.
• Help invent new features and contribute to technical direction for AI and data intensive services.
• Provide guidance and informal mentorship to junior engineers and peers learning AI and cloud patterns.

• Design, develop, test, and maintain high quality software services that meet program requirements and deliver measurable value to cancer researchers.
• Collaborate closely with fellow engineers, QA, product owners, and business users in an agile, iterative environment.
• Write and maintain clear technical documentation, contributing to shared coding standards and best practices.
• Support acceptance testing and ensure successful delivery to end users in secure, compliant environments.
• Troubleshoot and resolve production issues, including performance, reliability, and scalability challenges.
• Integrate existing software into new or updated cloud environments using modern deployment and containerization practices.
• Contribute to continuous improvement of team processes, development tooling, and engineering standards.

• Design and deploy AI powered features that enhance data discovery, interoperability, and research workflows across multiple platforms.
• Integrate LLMs and other ML models into cloud and web applications, including:
  Retrieval augmented generation (RAG) pipelines, tool and function calling, and prompt design and evaluation frameworks.
• Build and enhance data pipelines for model training and fine tuning using biomedical and clinical datasets.
• Implement monitoring and feedback mechanisms to manage model drift, accuracy, latency, cost, and user experience.
• Run experiments and evaluations to improve model performance and outcomes for researchers and clinicians.
• Apply responsible AI principles aligned with federal security, privacy, and compliance standards.
• Collaborate with federal stakeholders, domain experts, and UX teams to translate research needs into scalable, repeatable AI solutions.

• Bachelor’s degree in Computer Science or a related field from an accredited (CHEA) institution.
• 3-5 years of experience building and maintaining production software systems.
• Experience with modern web or service development stacks, for example:
  Languages such as Java, Python, Type Script, or similar; frameworks such as React, Angular, Node.js, or similar.
• Hands‑on experience in a cloud environment (AWS preferred), including containerized workflows (for example Docker, ECS, EKS).
• Experience with relational databases and SQL, and familiarity with modern CI/CD practices.
• Strong communication skills and experience working directly with customers, product owners, or business stakeholders.
• Proven ability to collaborate in a team environment, manage priorities, and deliver in a fast paced government or enterprise setting.

• Proficiency in Python for AI, ML, or LLM integration, plus at least one additional production language (for example Java, Type Script, Go, or C++).
• Applied knowledge of ML fundamentals such as evaluation, bias, data…