{"id":3132458,"url":"https://alion.io/job/spatial-front-ai-developer","title":"AI Developer","company":{"id":4414873,"name":"Spatial Front","domain":"spatialfront.com","url":"https://alion.io/company/spatial-front","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Paylocity","truth_index":{"grade":"B","score":75,"open_postings":23,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-24T06:02:35Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":120000,"max":155000,"currency":"USD","period":"year","gross":null,"usd_annual":155000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Boto3","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"Hallucination","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Oracle ERP","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"Structured Outputs","optional":false},{"name":"AWS","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"AWS Strands Agents","optional":true},{"name":"Azure","optional":true},{"name":"Azure DevOps","optional":true},{"name":"Docker","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"Least Privilege","optional":true},{"name":"Reranking","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-10-14T13:04:41Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-24T12:18:59Z","board_verified":true,"closed_at":null,"days_open":10,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":10},"description":"Description\nSpatial Front, Inc. (SFI) is seeking an AI Developer to support our growing modernization team. SFI was recently awarded the 2025 USA Today National Top Places to Work award and the 2025 Washington Post Top Workplaces. The ideal candidate will perform hands-on Python development of artificial intelligence agents and generative AI applications supporting PeopleSoft HCM and related enterprise systems.\nThis role is focused on building the intelligence and application behavior of production AI solutions, including agent design, prompt engineering, orchestration, retrieval-augmented generation (RAG), tool use, evaluation, and supporting Python services. The candidate will work closely with AI integration engineers, platform engineers, data engineers, functional teams, and cybersecurity personnel to develop reliable, secure, and maintainable AI capabilities for Federal Government customers.\nLocation\nCrystal City, VA - On-Site/Hybrid\nResponsibilities\nDesign, develop, test, and maintain AI agents and generative AI applications using Python.\nDevelop agent workflows that use large language models to interpret user requests, reason across multiple steps, select appropriate tools, retrieve information, and generate grounded responses.\nDesign, test, and iteratively refine system prompts, task instructions, tool-use instructions, few-shot examples, response formats, and other prompt-engineering components.\nApply prompt-engineering techniques to improve response accuracy, consistency, grounding, tool selection, adherence to business rules, and overall user experience.\nDevelop agent orchestration logic including state management, context management, tool selection, multi-step execution, retries, exception handling, and recovery.\nDevelop Python application components using boto3, botocore, and other appropriate SDKs and libraries to interact with AI, data, storage, security, and supporting cloud services.\nIntegrate agents with approved tools, APIs, Model Context Protocol (MCP) services, databases, knowledge sources, and enterprise applications.\nWork with AI Integration Engineers to define tool requirements, expected inputs and outputs, validation rules, and integration behaviors needed by AI agents.\nBuild and optimize retrieval-augmented generation (RAG) capabilities including query formulation, retrieval logic, context assembly, grounding, semantic search, and use of retrieved information within agent workflows.\nDevelop prompt and context strategies for working with structured and unstructured enterprise information while minimizing irrelevant or unsupported model responses.\nImplement structured outputs, schema validation, response validation, guardrails, error handling, and other controls required for reliable enterprise AI behavior.\nDevelop reusable Python libraries, utilities, agent components, prompts, and development patterns that can be used across multiple AI use cases.\nCreate automated unit, integration, regression, and AI evaluation tests covering agent workflows, prompts, tool selection, model responses, retrieval quality, and application behavior.\nDevelop and maintain evaluation methods for response quality, factual grounding, hallucination, tool-use accuracy, retrieval effectiveness, latency, consistency, and regression.\nAnalyze model and agent behavior using logs, prompts, responses, tool calls, retrieved context, and downstream results to identify and correct performance or reliability issues.\nCompare and evaluate models, prompting approaches, retrieval strategies, and agent designs based on accuracy, reliability, performance, maintainability, security, and suitability for the use case.\nCollaborate with functional experts and Product Owners to translate business problems into clearly defined AI use cases, expected behaviors, acceptance criteria, and measurable outcomes.\nSupport demonstrations, user testing, defect resolution, production validation, monitoring, and continuous improvement of deployed AI capabilities.\nMaintain source code, prompts, technical designs, configuration, evaluation criteria, support documentation, and other AI development artifacts.\nParticipate in code reviews, architecture discussions, backlog refinement, demonstrations, testing, release readiness, and other Agile/SAFe delivery activities.\nOther duties as assigned.\nRequirements\n Must possess an active Secret security clearance\nBachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Information Systems, Engineering, or a related field; equivalent relevant experience may be considered.\n3+ years of hands-on software development experience, including strong experience developing production-quality applications and services using Python.\nStrong Python development skills including object-oriented development, modules/packages, dependency management, exception handling, logging, testing, debugging, and API development.\nHands-on experience developing AI agents, agentic workflows, LLM-based applications, or comparable generative AI capabilities.\nStrong hands-on prompt engineering experience, including development and refinement of system prompts, task instructions, few-shot examples, structured outputs, grounding strategies, tool-use instructions, and context-management approaches.\nExperience developing applications that interact with large language models through APIs or SDKs.\nExperience developing agent workflows that use tools, APIs, or MCP-based services, including tool selection, structured inputs and outputs, error handling, and integration of tool results into agent behavior.\nHands-on experience using boto3 and botocore to develop applications that interact with cloud services and APIs.\nExperience with retrieval-augmented generation (RAG), semantic search, embeddings, vector search, or other knowledge-grounded AI techniques.\nExperience developing REST APIs, consuming APIs, working with JSON, and integrating Python applications with external services.\nExperience developing automated tests or evaluation methods for AI applications, including assessment of response quality, grounding, tool use, or regression behavior.\nExperience with Git/source control, code reviews, CI/CD, debugging, logging, and modern software-development practices.\nStrong troubleshooting skills and ability to diagnose problems across Python code, agent behavior, prompts, model responses, retrieval, tools, APIs, and downstream services.\nDesired Skills\nAdvanced experience developing agentic AI applications involving multiple tools, multi-step workflows, state management, or complex orchestration.\nExperience designing and optimizing production prompt libraries, agent instructions, reusable prompt templates, and prompt-evaluation approaches.\nExperience evaluating and mitigating hallucination, weak grounding, inconsistent responses, poor tool selection, excessive context, and other common LLM application problems.\nExperience with Amazon Bedrock, foundation-model APIs, or comparable managed generative AI services.\nExperience with agent-development frameworks such as LangGraph, LangChain, Strands Agents, or comparable orchestration technologies.\nExperience building RAG applications using embeddings, vector search, semantic retrieval, hybrid retrieval, document ingestion, or reranking.\nExperience with vector databases or Oracle AI Vector Search is a plus.\nExperience developing AI capabilities involving case management, knowledge management, help desk support, summarization, classification, recommendation, or workflow assistance.\nExperience consuming MCP services and integrating MCP tools into AI agent workflows.\nUnderstanding of AI application security considerations including prompt injection, inappropriate tool use, excessive agency, sensitive-data exposure, authentication, authorization, and least-privilege access.\nWorking knowledge of SQL and relational databases, particularly Oracle.\nExperience integrating AI applications with PeopleSoft HCM, PeopleSoft CRM, or other complex enterprise applications is a plus.\nExperience developing containerized Python applications using Docker and deploying applications into cloud or Kubernetes environments.\nFamiliarity with OCI or deployment of AI applications into Oracle Cloud Infrastructure is a plus.\nExperience working in Agile or SAFe environments and using Azure DevOps (ADO) or a similar lifecycle-management tool.\nExperience supporting secured Federal or DoD enterprise systems is preferred.\nPython, AWS, AI/ML, cloud, or related certification is a plus.\nAdditional Information\nClearance: Must be a U.S. Citizen with an active Secret security clearance.\n Work Environment: Onsite/Hybrid as required by the contract.\nFor information on SFI's benefits please visit http://www.spatialfront.com/pages/career.html\nThis is a full-time W2 position. \nPlease no agencies, third parties, or corp-to-corp.\nSpatial Front Inc. is an Equal-opportunity Employer, all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.\nSpatial Front Inc. participates in E-Verify.\nSalary Description\n$120,000-$155,000","description_format":"text","description_chars":9219,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":true,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United 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