AI / ML Developer

Peraton•,

2d•$86,000 - $138,000

About The Position

The role operates in a fast-paced innovation environment, rapidly developing and validating proof-of-concepts for advanced AI and machine learning use cases. It focuses on experimenting with emerging analytics tools, integrating them into a cloud-based analytics ecosystem, and helping transition successful concepts toward operational deployment. The work includes hands-on data exploration, prototyping, workflow automation, and contributing to secure, well-governed MLOps practices. The developer collaborates with engineering and product teams to evaluate emerging tools, build technical demonstrations, document integrations, and communicate platform updates.

Requirements

Software engineering background with deep experience building production-grade applications and services.
Expertise developing agentic AI systems, including planning, tool use, multi-step reasoning, workflow execution, or autonomous decision-making logic.
Hands-on experience designing and implementing MCP-based integrations, tool interfaces, or model-driven service frameworks.
Ability to translate ambiguous business or mission requirements into scalable AI-driven solutions while balancing technical feasibility and real-world impact.
Proficiency with LLM development practices, including fine-tuning, RAG integration, prompt engineering, and interaction models for agent workflows.
Strong Python development skills and familiarity with distributed compute environments, APIs, microservices, and cloud-native architectures.
Experience integrating agents or LLM-driven components into cloud platforms such as Azure, AWS, or GCP, or into large-scale data ecosystems.
Understanding of LLMOps/MLOps principles, including versioning, testing, deployment automation, monitoring, and governance for agentic systems.
Demonstrated ability to lead solution design, mentor developers, and communicate complex AI architectures to technical and non-technical stakeholders.
Experience with version control and modern CI/CD practices such as Git/GitHub, including automated testing, deployment pipelines, and release management for production systems.
U.S. citizenship and ability to obtain or maintain a Public Trust clearance.
Experience requirement: 5 years with a bachelor's degree, 3 years with a master's degree, or 0 years with a Ph.D.

Nice To Haves

Experience building or contributing to multi-agent systems or coordinated agent workflows.
Familiarity with frameworks such as LangChain, LlamaIndex, Strands Agents, LangGraph, or CrewAI.
Knowledge of evaluating agent performance, implementing guardrails, or designing safe action-execution patterns.
Hands-on work with vector databases, embedding models, or advanced retrieval techniques.
Experience integrating agentic components into large-scale analytics or data platforms such as Databricks or Snowflake.
Background in data exploration or data modeling to support agent-driven decision processes.
Experience building AI-focused prototypes or experimenting with emerging agentic patterns in rapid-iteration environments.
Familiarity with optimization techniques such as model routing, response validation layers, or inference acceleration.
Familiarity with systems that handle sensitive data in accordance with HIPAA and federal security standards, including encryption, access controls, auditability, and governance for protected health information in AI/LLM workflows.

Responsibilities

Develop and validate proof-of-concepts for advanced AI and machine learning use cases.
Experiment with analytics tools and integrate them into a cloud-based analytics ecosystem.
Help move successful concepts toward operational deployment.
Conduct data exploration, build prototypes, and automate workflows.
Contribute to secure and well-governed MLOps practices.
Collaborate with engineering and product teams to evaluate emerging tools and build technical demonstrations.
Document integrations and communicate platform updates.
Lead solution design, mentor developers, and communicate AI architectures to technical and non-technical stakeholders.