Agentic AI Engineer (Forward Deployment)

Agentic AI Engineer (Forward Deployment)

Experience: -8+ Years

Location: -3 days work from office

Edina, MN (1 st preference)

Chicago, IL (2 nd Preference)

Irving, TX (3 rd Preference)

What is in it for you?

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.

Roles and 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.mMaintain 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 behaviour, 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. Duration: -

Educational Qualifications: -

Engineering Degree BE/ME/BTech/MTech/BSc/MSc.

Technical certification in multiple technologies is desirable.

Skills: -

Mandatory skills

Strong hands-on Python and SQL development skills.

Demonstrated delivery of an LLM-enabled application beyond a basic chatbot or notebook demonstration.

Experience with tool/function calling, structured responses, retrieval-grounded generation, and AI evaluation.

Strong backend/API engineering skills, including validation, authentication, authorization, error handling, and automated testing.

Experience with Databricks or a comparable enterprise data platform, with the ability to work effectively in Databricks.

Understanding of relational databases, data modeling, audit trails, and transactional integrity.

Experience with cloud-hosted applications, preferably on Azure.

Proficiency with Git, code reviews, CI/CD, and reproducible deployment practices.

Ability to diagnose issues across application, data, integration, and infrastructure boundaries.

Strong stakeholder communication and the ability to work independently within a cross-functional delivery team.

Practical understanding of sensitive-data handling and secure enterprise development.

Preferred Qualifications

Experience with FastAPI, React, and TypeScript.

Familiarity with NestJS, Next.js, and PostgreSQL.

Experience with Databricks Apps, Jobs, Delta Lake, Unity Catalog, and asset bundles.

Experience with Azure Blob Storage, managed identities, and application monitoring.

Experience with an agent framework such as LangChain/LangGraph, Azure AI Foundry Agents, or an equivalent framework; willingness to use the approved project stack.

Familiarity with retrieval evaluation, knowledge-base versioning, and reviewer-feedback workflows.

Experience in healthcare data, data-use agreements, compliance-sensitive workflows, or enterprise data validation.

Experience using approved AI coding assistants while maintaining code provenance, testing, and human review.

Skills

RAG Pipeline, AgenticAI, Python, Azure