Make an Impact Where It Matters MostIn this role, you will design, build, evaluate, and deploy AI and machine learning solutions that help teams work with complex data, automate analysis, and solve difficult mission problems.
You will work across generative AI, agentic systems, machine learning, data science, and production software engineering. From building LLM-powered applications and intelligent agents to developing predictive models and production data pipelines, you will help take ideas from prototype through deployment while keeping performance, reliability, security, and scalability in focus.What Makes This Opportunity Stand Out
This role gives you the chance to work across the full AI lifecycle rather than owning one narrow piece of it. You will build agents, experiment with new models, develop traditional machine learning solutions, work with large and complex datasets, design evaluation strategies, and help determine which approaches are ready for production.
The work is especially well suited for someone who understands that successful AI engineering is more than connecting an application to an LLM. You will think about retrieval, orchestration, tool use, evaluation, latency, cost, data quality, model behavior, production monitoring, and the software engineering required to make AI dependable.
At Intelliforce, your work is visible and your technical judgment matters. Because we support prime programs, our engineers stay close to meaningful mission problems and the people using what they build. Because we are a small business, leadership is accessible, communication is direct, and strong engineering gets noticed. Add in long-term stability, strong benefits, and a people-first culture, and this is an opportunity to help shape how modern AI is applied to real mission challenges.Here's What Your Day-to-Day Could Include
- Design, build, and deploy AI agents and agentic workflows using large language models
- Develop LLM-powered applications using tool and function calling, RAG, memory, orchestration, and multi-step workflows
- Evaluate AI systems for accuracy, reliability, latency, cost, and overall quality
- Build machine learning models for prediction, classification, recommendation, forecasting, anomaly detection, and optimization
- Perform exploratory data analysis, statistical analysis, feature engineering, and experimentation
- Develop pipelines that prepare structured and unstructured data for AI and ML applications
- Design automated and human-in-the-loop evaluation strategies for LLM and machine learning systems
- Create benchmarks, test datasets, and evaluation frameworks to measure model and application performance
- Prototype new AI and ML capabilities and assess their readiness for production use
- Deploy, monitor, maintain, and improve AI and ML services in production environments
- Work with engineering, product, and mission stakeholders to translate difficult problems into practical technical solutions
- Integrate AI and ML components into larger software systems and operational environments
- Debug performance, data, integration, and model-quality issues
- Recommend new technologies and engineering approaches for complex AI software efforts
Minimum Qualifications- Clearance: Top Secret Clearance with Full Scope Polygraph, must be active. We do not have the ability to sponsor clearances
- Citizenship: Must be a U.S. Citizen. We cannot sponsor visas
- 20 years of Software Engineering experience on programs and contracts of similar scope, type, and complexity
- Bachelor's degree in Computer Science or a related discipline from an accredited college or university
- Four additional years of Software Engineering experience on projects with similar software processes may be substituted for the bachelor's degree
- Five years of demonstrated cloud experience supporting distributed scalable data technologies and related processing environments
- Strong professional experience developing production software and AI or machine learning solutions
Required Skills- Strong proficiency in Python
- Experience with common machine learning and data science libraries
- Hands-on experience developing applications with LLMs and generative AI models
- Experience building AI agents, including orchestration, tool or function calling, prompt design, and workflow design
- Experience working with commercial LLM APIs and/or open-source language models
- Strong understanding of retrieval-augmented generation, embeddings, vector search, and semantic retrieval
- Strong foundation in supervised and unsupervised machine learning
- Experience with model selection, feature engineering, validation, and evaluation
- Strong data science skills including statistical analysis, experimentation, data exploration, and visualization
- Experience working with SQL and large or complex datasets
- Experience with machine learning frameworks such as scikit-learn, PyTorch, TensorFlow, or similar tools
- Experience deploying AI or ML solutions into production environments
- Understanding of APIs, Git, automated testing, CI/CD, and cloud-based software development
- Experience with distributed scalable NoSQL or Big Data stores
- Experience with MapReduce programming models and related technologies
- Experience with Hadoop Distributed File System, HDFS
- Experience with serialization formats such as JSON or BSON
- Strong software engineering, debugging, and problem-solving skills
Desired Skills- Experience with agent frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, or similar technologies
- Experience with vector databases and search technologies
- Experience with MLOps, model monitoring, and production machine learning infrastructure
- Experience with AWS, Azure, or Google Cloud AI and ML services
- Experience fine-tuning or adapting models for specific use cases
- Experience working with open-source LLMs
- Familiarity with AI safety, guardrails, observability, and responsible AI practices
- Experience rapidly prototyping AI applications and transitioning successful prototypes into scalable production systems
- Experience designing evaluation harnesses for generative AI systems
- Experience with human-in-the-loop AI workflows
- Experience optimizing model inference for latency, throughput, and cost
- Experience leading or providing technical direction for complex software development efforts
Compensation Range: $196,000.00 - $252,000.00
This salary range reflects our current market estimate for the role. Final compensation will depend on your experience, technical background, overall qualifications, and alignment with the position. We evaluate each candidate thoughtfully, so offers are shaped around the value you bring to the teamYou can reach us at [email protected] or schedule a call with our Director of Recruitment, just visit this link to view their calendar: https://calendly.com/amwolfe-intelliforce-itsg/30min .
