Role: Senior Agentic AI Developer / Forward Deployed Engineer
Location: Eden Prairie, MN, Chicago, IL (Onsite)
Job Description:
Need 10+years
Mandatory Skills RAG Pipeline, AgenticAI, Python, Azure
We are seeking a hands-on Senior Agentic AI Developer / Forward Deployed Engineer to design, build, integrate, and operationalize enterprise applications.
The engineer will work closely with business stakeholders, domain experts, product owners, architects, data engineers, quality engineers, and platform teams to translate business problems into secure, testable, and supportable solutions.
This is an embedded delivery role with end-to-end implementation ownership, covering discovery, technical design, application and data development, AI integration, evaluation, deployment, and operational readiness. The successful candidate must be equally comfortable implementing deterministic business rules and developing governed AI-assisted capabilities. This is not solely a prompt-engineering, model-research, or architecture-advisory position.
Recommended experience 6+ years in software/data engineering, including 2+ years delivering LLM-enabled applications or equivalent demonstrable experience
Key Responsibilities
- Embedded Discovery and Solution Delivery Partner directly with business users and subject-matter experts to understand workflows, pain points, exceptions, and desired outcomes. Translate business requirements into technical specifications, user stories, testable acceptance criteria, and incremental delivery plans. Identify when deterministic logic, conventional automation, retrieval, or an AI agent is the appropriate solution. Build working vertical slices that demonstrate business value rather than disconnected proofs of concept. Communicate technical trade-offs, risks, dependencies, and decisions clearly to technical and nontechnical stakeholders.
- Agentic AI and LLM Application Engineering Develop bounded AI capabilities for research, explanation, recommendation, and workflow assistance. Implement structured outputs, context retrieval, tool/function calling, prompt versioning, and output validation. Design tool contracts with explicit permissions, schemas, failure behavior, and audit requirements. Ground AI responses in approved, versioned sources and preserve links to supporting evidence. Implement human review, escalation, abstention, and fallback behavior. Enforce authorization boundaries in application code and tool permissions—not through prompts alone. Evaluate model and framework options against approved enterprise requirements for quality, security, latency, and cost.
- Software and Data Engineering Build production-quality backend services, APIs, data-processing components, and workflow integrations. Develop Databricks notebooks/jobs and SQL transformations for ingestion, normalization, matching, comparison, and validation. Implement data-quality checks, schema validation, deduplication, reconciliation, and versioned processing rules. Maintain reproducibility through pinned rule sets, input references, and traceable execution records. Collaborate on reviewer-facing interfaces and end-to-end application behavior. Engineer reliable integrations using appropriate retry, idempotency, timeout, and recovery patterns.
- Quality, Evaluation, and Verification Create unit, integration, end-to-end, regression, and negative tests. Develop representative evaluation datasets with domain experts. Assess AI outputs for grounding, correctness, usefulness, safety, and appropriate abstention. Test unauthorized tool use, prompt injection, sensitive-data exposure, and failure scenarios. Trace delivered functionality and verification evidence to acceptance criteria. Review AI-generated code with the same rigor as manually written code.
- Security, Governance, and AI-DLC Follow the applicable AI-DLC lifecycle, approved tooling paths, review requirements, and release controls. Implement least-privilege access, secure authentication, secrets management, and data-access restrictions. Use synthetic, mock, or approved de-identified development data where required. Maintain architecture decisions, prompt/tool documentation, approval evidence, and delivery records. Escalate unresolved data classification, privacy, model approval, and risk decisions to accountable owners. Preserve human authority over final business decisions and production promotion.
- Operational Readiness Instrument application behavior, retrieval, tool calls, model usage, failures, and dependencies. Establish prompt/model traceability and appropriate telemetry protections. Implement usage and cost attribution, monitoring, s, and agreed reliability measures. Produce deployment guidance, rollback procedures, runbooks, and support handover materials. Work with platform and operations teams while retaining delivery-team accountability for application quality and remediation.
Required Qualifications and Skills
- Strong hands-on Python and SQL development skills. Demonstrated delivery of an LLM-enabled application beyond a basic chatbot or notebook demonstration.
