Agentic Workflows

Agentic Workflows Engineer Jobs

4 jobs found

Today

Staff Applied AI Engineer

DOSS San Francisco, CA

San Francisco, CA

Today

$230k - $260k/yr Full time Agentic Ai Agentic Workflows Ai Agent

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Job Description

The Staff Applied AI Engineer role at DOSS is responsible for end-to-end applied AI that powers Dossbot, AI-assisted schema and workflow generation, and agentic automations grounded in live operational data. This position focuses on production architecture, evaluation and safety, and building enterprise workflows that remain tenant-isolated and auditable.

Responsibilities

Own the applied AI architecture end-to-end, including model selection, orchestration, RAG over the unified operational data model, production evals and guardrails, plus cost and latency budgets.
Develop Dossbot as the primary operations interface, enabling natural-language search, analysis, and automation grounded in live supply-chain, finance, and ops data.
Build AI-assisted tooling that uses foundation models to propose, validate, and apply customer schemas and workflow definitions directly on live production tenants.
Design agentic workflows that operate within enterprise guardrails, including permission-aware execution, auditability, tenant isolation, and safe runs against a customer system of record.
Stand up evaluation and observability infrastructure to measure quality, detect regressions, and proactively identify anomalous or risky emergent behavior.
Partner with product, design, and the Forward Deployed team to deliver high-leverage AI use cases.
Mentor and shape engineering culture as the center of gravity for applied AI, raising standards for how the team builds, evaluates, and ships LLM-powered systems.

Requirements

Shipped LLM-powered features or agents to production and operated them at scale, with experience beyond prototypes or demos.
Strong hands-on experience with foundation model APIs, RAG pipelines, tool use and function calling, prompt and context engineering, and fine-tuning when it is warranted.
Experience building evaluation harnesses and guardrails for non-deterministic systems working with business-critical data.
Solid backend fundamentals, including system design, Postgres, and cloud services for reliable multi-tenant web applications.
Ability to go from 0 to 1 in ambiguous settings, with sound judgment on when AI is the right approach and when it is not.
Collaborative working style, bringing positivity, openness, and curiosity to the team.
Growth mindset focused on continuous learning and improvement for self, team, and company.
Craftsmanship and attention to quality in delivered systems.
Customer-first orientation focused on the customer experience journey and building a product intended to be genuinely delightful.
Pragmatic decision-making based on the situation, not theory.
Low-ego collaboration, with effectiveness in environments where team members support one another.

Technologies

RAG
Postgres
Foundation model APIs
Tool use and function calling
Fine-tuning
LLM

Compensation

USD 230,000 - 260,000 per year.

Location

San Francisco, CA (onsite).

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3d ago

AI Engineer V (Principal)

AssistRx, LLC Miami, FL

Miami, FL

3d ago

$162k - $204k/yr Full time Senior Agentic Ai Agentic Workflows

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Job Description

Define and scale enterprise AI across the specialty medication access ecosystem as a senior individual-contributor technical authority.

Responsibilities

Set the multi-year AI technical strategy and roadmap with the Vice President, Engineering, Product, and Architecture; align AI investments to business value, feasibility, and risk
Lead architecture and delivery of complex enterprise AI/ML solutions, including generative AI, predictive models, and intelligent document processing across platforms, products, and clients
Architect LLM applications using retrieval augmented generation, prompt engineering, structured outputs, and function and tool calling with agent-based orchestration
Establish patterns for agentic workflows (task decomposition, tool/function calling, memory and state management, routing, boundaries, human review checkpoints, and fallback paths) for high-impact decisions such as benefits verification and prior authorization
Identify new AI opportunities and emerging technologies; lead proofs of concept that move proven pilots to scale
Choose the right approach per problem (prompting, retrieval augmentation, tools, task-specific models, or deterministic logic when models are not appropriate)
Define standards for AI design documentation, including intended behavior, context and retrieval design, prompt and agent structure, evaluation approach, and fallback behavior
Own the enterprise AI reference architecture, platform strategy, and engineering standards (model access, prompt/version management, retrieval services) and ensure adoption across engineering teams
Lead evaluation of commercial models, AI platforms, and vendor tools; recommend build-versus-buy options to executives using quality, cost, security, strategic fit, and time-to-value
Guide vendor and cloud provider relationships technically (model selection, capacity, pricing, and risk)
Anticipate scaling, reliability, and cost challenges; direct architectural investments to address issues before client impact
Set organization targets for accuracy, groundedness, hallucination rate, task completion, latency, and cost; guide model selection, tuning, and evaluation
Define evaluation strategy, benchmark datasets, and automated test frameworks to measure output quality and detect regressions before release
Improve retrieval pipelines via optimization of embeddings, chunking strategy, and inference performance to raise quality and reduce cost per transaction
Direct error analysis and human-in-the-loop review with operations and clinical subject matter experts; ensure findings improve prompts, models, and guardrails
Establish experimentation approach, including A/B testing across prompts, retrieval configurations, model selection, and workflow design
Define quality gates and regression thresholds so AI features meet accuracy and safety requirements before release
Set technical direction for pipelines preparing structured and unstructured healthcare data (enrollment forms, payer documents, clinical notes) for training, retrieval, and inference
Partner with Data Engineering and Architecture on enterprise data contracts, feature sets, vector stores, and lineage for AI workloads
Define enterprise standards for de-identification, masking, and data minimization when working with protected health information
Define AI workload standards for CI/CD, source control, containerization, and infrastructure-as-code practices
Establish versioning, promotion, and rollback practices for prompts, agents, and model configurations as versioned artifacts alongside application code
Own the observability strategy for accuracy drift, groundedness, task completion, token consumption, latency, cost, and reliability, including alerting and incident response practices
Own model and inference spend strategy using caching, routing, and model right-sizing
Serve as final technical escalation for critical production issues and client-reported defects; drive systemic improvements to prevent recurrence
Lead responsible AI approach and ensure compliance with healthcare privacy and security needs, including HIPAA and HITRUST
Partner with Security, Compliance, and Legal on AI-specific risks (prompt injection, data leakage, insecure output handling) and confirm vendor agreements (including Business Associate Agreements) before PHI processing
Define guardrails, content filtering, human oversight, and audit trails based on the risk profile of each use case
Contribute to internal AI governance; represent AssistRx AI practices in audits, client due diligence, and security reviews
Track emerging AI regulation and industry guidance; translate into engineering policy and practice
Advise Vice President, Engineering and executive leadership on AI strategy, risk, investment, and capability
Provide technical leadership across engineering teams by influencing enterprise architecture and engineering direction without direct authority
Mentor Staff, Senior, and mid-level engineers; build the AI technical bench and increase AI fluency across the company
Lead cross-functional technical initiatives and resolve complex technical disagreements across teams
Work with Product Management, Client Services, and business leaders to identify high-value AI use cases and define success measures prior to build
Communicate AI capabilities, limitations, risks, and results in plain language to technical and non-technical audiences, including executives, clients, and external partners
Contribute to hiring strategy, interviewing, and role definition for AI and related engineering roles
Advance AI-assisted and agentic development practices, including specification-driven workflows and agentic coding tools; share what works with engineering peers
Participate in agile activities as needed (sprint reviews, retrospectives) and perform regular self-guided study

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, or related technical field (or equivalent experience); Master’s preferred
10+ years of software engineering experience, including 6+ years building and deploying AI or machine learning solutions in production
Experience leading strategy and delivery of enterprise-scale AI used by real customers or operations teams, with significant measurable business outcomes
Expert programming skills in Python, plus experience in .NET/C#/Java (or comparable enterprise language); backend services and REST APIs (e.g., FastAPI, Flask, ASP.NET)
Hands-on expertise with large language models and generative AI patterns: retrieval augmented generation, prompt engineering, embeddings, vector/hybrid search, reranking, structured outputs, function/tool calling, context window management, and model evaluation
Extensive experience architecting and operating production AI workloads on cloud AI/ML platforms (AWS preferred, including Amazon Bedrock and Amazon SageMaker; equivalent Azure experience acceptable via Azure AI Foundry, Azure OpenAI Service, or Azure Machine Learning)
Expert knowledge of APIs, microservices, distributed systems, SQL, and modern practices including Git, CI/CD, containerization, and automated testing
Ability to deliver in environments where privacy, security, and regulatory requirements shape solution design
Demonstrated enterprise-level technical leadership: set technical strategy, drive architectural decisions across teams, and develop other technical leaders
Ability to operate independently on highly ambiguous, high-impact problems; set direction and make sound tradeoffs with limited precedent
Hands-on experience building with agentic coding tools (e.g., Claude Code, GitHub Copilot agents, Codex, Cursor), including specification-driven workflows and multi-step agent execution
Practical understanding of production LLM behavior: hallucinations, prompt sensitivity, non-determinism, latency and cost tradeoffs
Expertise in LLM evaluation methods: automated scoring, LLM-as-judge, and human review workflows
Exceptional written and verbal communication to influence executives, clients, and engineers and represent the company’s AI approach externally

Preferred Qualifications

Master’s or doctoral degree in Computer Science, Machine Learning, Data Science, or related field
Experience in healthcare technology, specialty pharmacy, HUB services, ePA, or EHR/EMR integrations
Experience with intelligent document processing, OCR, and extraction from unstructured enrollment or clinical documents
Applied machine learning or data science background: experiment design, model evaluation, and error analysis
Experience with Docker, Kubernetes, or infrastructure-as-code at scale
Experience with agent and orchestration frameworks: LangChain, LangGraph, LlamaIndex, Semantic Kernel, Strands, or Model Context Protocol
Experience with MLOps tooling, model registries, feature stores, and LLM observability or evaluation platforms
Experience with model fine-tuning or smaller task-specific models for classification and extraction
Familiarity with AI security guidance such as OWASP Top 10 for LLM Applications
Familiarity with HIPAA, HITRUST, or responsible AI frameworks such as the NIST AI Risk Management Framework
Prior experience establishing an AI function or serving as the senior AI technical leader for an organization
Experience representing technical capabilities in client due diligence, security reviews, or external audits

Technologies

Python; .NET; C#; Java; FastAPI; Flask; ASP.NET
Retrieval augmented generation; prompt engineering; structured outputs; function and tool calling; agent-based orchestration
Embeddings; vector search; hybrid search; reranking; context window management
LLM-as-judge; AWS; Amazon Bedrock; Amazon SageMaker; Azure AI Foundry; Azure OpenAI Service; Azure Machine Learning
APIs; microservices; distributed systems; SQL; Git; CI/CD; containerization; automated testing
Claude Code; GitHub Copilot agents; Codex; Cursor

Location and Compensation

Location: Florida (onsite)
Salary: USD 162,239 - 204,048 per year

Competencies

Technical Depth; Strategic Thinking; Analytical Rigor & Problem Solving
Experimentation & Measurable Outcomes; Accuracy & Quality
Sound Judgment & Risk Awareness; Ownership & Accountability
Pragmatism & Bias for Delivery; Communication; Collaboration & Influence
Technical Leadership & Talent Development; Curiosity & Learning Agility
Adaptability; Initiative & Innovation; Ethics & Integrity

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11d ago

AI Engineer Associate Consultant

ZS Princeton, NJ

Princeton, NJ

11d ago

Full time Backend Developer Agentic Workflows Ai Engineer

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Job Description

ZS is seeking an AI Engineer Associate Consultant to help design and deliver scalable, distributed software systems and LLM-based pipelines. This hybrid role in Princeton, NJ focuses on end-to-end execution, from architecture and deployment to governance, responsible AI guardrails, and DevOps practices across cloud environments.

What You’ll Do

Own the architecture design and development of scalable and distributed software systems.
Translate business needs into technical requirements.
Drive technical execution with a focus on code quality, meeting deadlines, and efficient resource allocation.
Use data-driven decision making to support product goals.
Design, develop, and deploy LLM pipelines using approaches such as RAG and agentic workflows, including PEFT methods (for example, LoRA, QLoRA).
Lead the full software development lifecycle, including requirements analysis, design, coding, testing, and deployment.
Leverage AWS and Azure services including IAM, Monitoring, Load Balancing, Autoscaling, Database, Networking, storage, ECR, AKS, and ACR.
Apply Databricks capabilities, especially Unity Catalog, and architect the governance layer from data through AI model output.
Design, develop, and deploy prompt and response guardrails aligned to responsible AI requirements.
Implement DevOps practices using Docker and Kubernetes to support continuous integration and delivery, including automation and monitoring scripts.
Collaborate with cross-functional teams, perform code reviews, and provide guidance on software design and best practices.

What You Bring

Bachelor’s degree in computer science, Information Technology, or a related field (or equivalent work experience).
Strong coding skills in Python and Javascript.
Experience with API frameworks for both stateless and stateful applications, such as FastAPI and Django.
Proficiency in cloud platforms, specifically AWS, Databricks, and Azure.
Hands-on experience with Infrastructure-as-Code, especially Terraform and CloudFormation.
Knowledge of front-end development (preferred): React JS, Next JS, Tailwind CSS.
Strong experience with LLM patterns including RAG, Vector DB, Hybrid Search, agent development, agentic workflows, and prompt engineering.
Experience with LLM APIs and ecosystems such as OpenAI, Anthropic, and AWS Bedrock, plus SDKs like LangChain and DSPy.
Hands-on DevOps experience with Docker, Kubernetes, and AWS services including Redshift, RDS, and S3.
Hands-on experience with Databricks solutions, including Unity Catalog.
Experience in production deployments supporting thousands of users, including capabilities such as Redis and Vector Search.
Strong understanding of scalable application design, plus security best practices and compliance with privacy regulations.
Knowledge of software engineering practices including GIT, DevOps (Azure DevOps preferred), and Agile or Scrum.
Strong communication skills to convey complex technical concepts to diverse audiences.
Experience with SDLC and best practices while developing.
Experience with Agile methodology for continuous product development and delivery.

Technologies

Python, Javascript, Fast API, Django
AWS, Azure, Databricks
Terraform, Cloudformation
React JS, Next JS, Tailwind CSS
RAG, Vector DB, Hybrid Search, Agent development, Agentic workflows, prompt engineering
Open AI, Anthropic, AWS Bedrock, Langchain, DSPy
Docker, Kubernetes, Redshift, RDS, S3
Unity Catalog, Redis, Vector Search
GIT, Azure DevOps, Agile, Scrum
IAM, Monitoring, Load Balancing, Autoscaling, ECR, AKS, ACR
PEFT, LORA, QLORA, Databricks Unity Catalog, ECS

Benefits

Comprehensive total rewards package supporting health and well-being, financial future, time away, and professional development.
Robust skills-building programs.
Multiple career progression paths.
Internal mobility.
Deeply collaborative culture.

How You’ll Grow

Cross-functional skills development and custom learning pathways.
Milestone training programs aligned to career progression opportunities.
Internal mobility paths that empower growth via s-curves, individual contribution, and role expansions.

Working Model

Hybrid: ZSers are onsite at clients or ZS offices three days a week.
Remote flexibility: work remotely two days a week is available.
ZS is committed to a Flexible and Connected way of working.

Travel

Travel is required for client-facing ZSers.
While some projects may be local, client-facing ZSers should be prepared to travel as needed.

Work Authorization

This position is not eligible for visa sponsorship.
Candidates must have authorization to work in the United States that does not now or in the future require employer sponsorship.

How to Apply

Submit an online application, including a full set of transcripts (official or unofficial).

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19d ago

AI Engineer

CCC Intelligent Solutions Inc. Chicago, IL

Chicago, IL

19d ago

$119k - $150k/yr Full time Agentic Workflows Ai Engineer Artificial Intelligence

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Job Description

CCC Intelligent Solutions Inc. is hiring an AI Engineer to help build and deploy LLM-powered capabilities that automate claim tasks with measured accuracy, privacy, and security in mind. You will own features end to end, spanning evaluation and guardrails, durable event-driven services, tracing and auditing, and NLP pipelines that convert unstructured claim data into structured outputs.

This role is based in Chicago, IL (onsite), and you will collaborate with product managers and data scientists from early scoping through production traffic, including defining the metrics used to judge impact.

What you’ll do

Ship LLM-powered features and agentic workflows using Python and TypeScript, partnering with product managers and data scientists from problem definition through production traffic, and helping set the metrics you will be measured against.
Develop evals, guardrails, and feedback loops that make model-driven features trustworthy, with explicit token and latency budgets.
Use agentic coding tools daily and own review of generated code, focusing on correctness, security, and design before code is opened in a pull request.
Maintain and extend NLP pipelines that transform unstructured claim data into structured data customers can act on, combining semantic search, classical NLP, and LLMs.
Build and operate durable, event-driven services where agents run as steps in deterministic workflows, using checkpointed model outputs so retries do not re-invoke completed LLM steps.
Trace every model call and agent step to support reconstruction and auditing, including citations back to source documents, and wire tracing into alerts.
Model and query claims data across relational, document, and graph stores.
Design privacy and security from the start, including tenant isolation, least-privilege access for agents and tools, and clear rules governing what personal and medical data may reach a model provider.
Write a technical design document for significant changes and use it to gather feedback and obtain team buy-in before building.
Participate in a shared on-call rotation for the services you build.

What you bring

3+ years of professional software engineering experience, including at least one LLM-powered feature you shipped and operated in production.
Strong Python and TypeScript experience, with typed, tested, async code in both and real depth in at least one.
Hands-on experience designing evolutions for LLM outputs (e.g., evals) and shipping guardrails.
A clear account of how you constrain agentic coding tool context and verify outputs, including experience with tools such as Claude Code, Cursor, GitHub Copilot, or similar in production work.
Experience building pipelines that blend LLMs with semantic search and classical NLP, including structured output, tool calling, embedding-based retrieval, and at least one example where classical methods outperformed an LLM.
Experience with message- or event-driven architectures, long-running workflows with retries, idempotent handlers, and event-sourced state, including running an LLM or agent step inside one.
Backend data modeling experience across PostgreSQL, MongoDB, Neo4j, SQL Server, and Cosmos DB, with depth in at least one relational and one non-relational store, plus familiarity with tenant isolation and regulated personal data.
Experience building and operating backend services on Azure or AWS, and familiarity with durable workflow engines such as Temporal or Durable Task Scheduler.
Experience writing technical design documents to obtain feedback and buy-in, and evidence that the team built to those plans.
Clear technical writing and discussion skills for both technical and non-technical audiences, along with strong code review collaboration.

Technologies you may work with

Python, TypeScript
Claude Code, Cursor, GitHub Copilot
Semantic search, classical NLP, LLMs
Embedding-based retrieval, structured output, tool calling
Message- or event-driven architectures, long-running workflows, idempotent handlers, event-sourced state
PostgreSQL, MongoDB, Neo4j, SQL Server, Cosmos DB
Tenant isolation, Azure, AWS
Temporal, Durable Task Scheduler
React, Terraform, Bicep
LangGraph, Microsoft Agent Framework
Model Context Protocol (MCP), Agent2Agent (A2A) protocol
Microservices, LangSmith, Arize, Braintrust
OpenTelemetry, CI/CD, A/B tests

Compensation and benefits

Salary: USD 119,030 - 150,000 per yearly
401K Match
Paid time off
Annual Incentive Plan Performance Bonus
Comprehensive health insurance
Adoption Assistance
Tuition Reimbursement
Wellness Programs
Stock Purchase Plan options
Employee Resource Groups

Even better if you have

React and TypeScript experience to deliver full-stack features end to end, including chat, streaming, and citation-heavy AI experiences.
Experience with CI/CD pipelines and infrastructure as code such as Terraform or Bicep.
Experience with agent frameworks including LangGraph and the Microsoft Agent Framework, and knowledge of how agents reach tools via MCP, A2A, command-line tools, and microservices.
Experience with LLM observability and evaluation tooling such as LangSmith, Arize, or Braintrust, and OpenTelemetry-based tracing.
Experience designing A/B tests for AI features or what-if simulations that replay historical claims before release.
Exposure to insurance, claims, or automotive repair domains.

Interview policy and privacy notice

A video interview is required for this position.
Video interviews are transcribed.
Transcriptions are retained and may be reviewed by CCC and their recruiters.
Candidates are not permitted to use generative AI or automated assistance during the interviews unless explicitly allowed by the interview team for a specific exercise.
The Job Applicant Privacy Notice is available HERE.

Success looks like: within six months you ship an LLM-powered feature with evals running in CI and can name the metric it moved; evals catch regressions caused by model migrations or prompt changes before customers do; you take ownership from concept to production with light direction; your services handle retries, partial failures, and model migrations without customer-visible incidents; production traces let you pinpoint the step and model call and support auditing; privacy and security reviews find no issues outside the intended design; your patterns spread through the team; you contribute to scoping and technical discussion with clarity; and you value measured outcomes, collaboration, and empathy.

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Agentic Workflows Engineer Jobs

Staff Applied AI Engineer

AI Engineer V (Principal)

AI Engineer Associate Consultant

AI Engineer

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