Senior AI Engineer II

DataJobs; Atlanta (GA); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Bonus opportunities

401k with company match

Employee Stock Purchase Plan

Paid vacation and sick time

Paid holidays

Job summary

DataJobs is seeking a senior software engineer to design, build, and operate an agentic AI framework on Microsoft Azure for end-to-end business impact. You will design reusable platform components, implement MCP integrations, and ensure secure, observable deployments across teams.

The role requires 7–10 years of backend experience with cloud-native architectures, AI tooling such as Azure OpenAI and Azure AI Search, and hands-on experience with LangChain/LangGraph.

Qualifications

• 7-10 years of professional software engineering experience.
• Deep backend proficiency in C#/.NET, Java, Node.js/TypeScript, or Python.
• Experience with service-based and microservice architectures and RESTful APIs.
• Production experience with cloud-based serverless microservices (Azure Functions).
• Event-driven architecture with queues, pub/sub, and retries.
• Relational and NoSQL data modeling and transactional correctness.
• CI/CD, automated testing, and code quality ownership.
• Shipped and operated agentic solutions in production.
• Knowledge of Azure OpenAI, Azure AI Search, and related tooling.
• Familiarity with LangChain/LangGraph and MCP patterns.
• Strong communication with technical and non-technical partners.

Responsibilities

• Design, build, and evolve a shared agentic framework for teams to deploy agentic solutions.
• Provide reusable platform blocks including scaffolding, interfaces, and observability.
• Convert one-off agent work into reusable components and deployment pipelines.
• Maintain MCP integrations with enterprise systems for live data access.
• Define standards, contracts, and reference architectures for agentic workloads.
• Ensure reliability, security, and observability for AI services on Azure.
• Deliver end-to-end production agentic solutions from framing to operations.
• Apply appropriate tool calling and multi-step planning approaches per use case.
• Incorporate retrieval, memory, and human-in-the-loop review as needed.
• Mentor peers, conduct code reviews, and participate in agile ceremonies.

Skills

Backend engineering

REST APIs

Asynchronous messaging

Agile/scrum

Angular

Communication

Education

Bachelor's degree in Computer Science or Engineering

Tools

Azure Functions

Azure OpenAI

Azure AI Search

LangChain

LangGraph

MCP

Claude Code

GitHub Copilot

Terraform

Bicep

Angular

Ragas

Langfuse

Job description

Design, build, and operate an agentic AI framework and production solutions on Microsoft Azure for end-to-end business impact.

Responsibilities

• Design, build, and evolve a shared agentic framework for teams to build, deploy, and operate agentic solutions across the organization
• Provide reusable platform building blocks including agent scaffolding, tool and integration interfaces, context and memory management, evaluation, guardrails, and observability
• Convert one-off agent work into reusable components such as service templates, harness components, MCP server patterns, evaluation harnesses, and deployment pipelines
• Design and maintain MCP integrations with enterprise systems (inventory, merchandising, and downstream systems) so agents can use live operational data without rebuilding integrations
• Define and document standards, contracts, and reference architectures used for agentic workloads
• Make architectural decisions for reliability, scalability, security, and observability for AI services deployed in Azure
• Deliver production agentic solutions end to end, from problem framing through deployment and ongoing operations, using the work to harden the platform
• Apply the right approach per use case: tool calling, multi-step planning, retrieval, memory, human-in-the-loop review, or deterministic services when agents are not appropriate
• Build and tune retrieval when warranted, including chunking strategies, vector indexing, retrieval ranking, and context engineering using Azure AI Search
• Contribute across the stack, including an Angular front end and a Python-based service and LLMOps layer
• Support partner teams transitioning mature agentic products with documentation, runbooks, and knowledge transfer
• Establish evaluation and regression testing as a first-class platform capability using eval sets, LLM-as-judge scoring, task-level success metrics, and CI regression gates
• Own evaluation pipelines for retrieval-based components using Ragas to track faithfulness, answer relevance, and context precision across releases
• Instrument agentic systems with Langfuse alongside Azure Monitor to enable tracing, latency, token and cost attribution, tool-call success rates, quality signals, and failure mode analysis
• Manage AI cost and performance through token budgeting, caching, model routing, right-sizing, and latency optimization
• Own production services including on-call participation, incident response, and follow-through after incidents
• Troubleshoot complex issues across agent behavior, retrieval quality, hallucination, latency, and integration reliability
• Use spec-driven development by turning ambiguous requests into clear specifications and acceptance criteria, keeping specs and implementation synchronized
• Set and model standards for AI-augmented development, including review discipline for agentic coding tools
• Conduct code reviews and mentor peers through technical influence and continuous learning
• Partner with product managers and business stakeholders to identify automatable workflows, translate them into technical requirements, and help prioritize the backlog
• Act as a technical consultant to teams adopting the platform so solutions are built effectively on top of it
• Participate in agile ceremonies including sprint planning, retrospectives, and daily stand-ups
• Communicate technical trade-offs and architectural decisions to both technical and non-technical audiences
• Partner with security, data, and platform teams on data governance, PII handling, prompt injection defense, and responsible use
• Evaluate emerging agent capabilities, tooling, protocols, and Azure OpenAI / AI Foundry updates, and recommend and prototype improvements to keep the platform current
• Establish and maintain engineering best practices including CI/CD pipelines, infrastructure as code, code quality standards, and security practices for AI workloads
• Continuously reduce time and cost to bring the next agentic solution to production

Requirements

• 7-10 years of professional software engineering experience with increasing scope and ownership
• Deep backend engineering proficiency in at least one of: C#/.NET, Java, Node.js/TypeScript, or Python
• Experience with service-based and microservice architectures, RESTful API design, and asynchronous service communication including API versioning, contract design, error semantics, and backward compatibility
• Production experience with cloud-based serverless microservices (Azure Functions, Container Apps, or equivalent)
• Event-driven architecture experience including queues, pub/sub, idempotency, retries, and dead-letter handling
• Solid data fundamentals including relational and NoSQL data modeling, query performance, and transactional correctness
• Demonstrated code quality ownership with automated testing, code review, and CI/CD as normal practice
• Shipped and operated agentic solutions in production (not prototypes or POCs only)
• Hands-on harness engineering for production model scaffolding: context construction and management, tool/function-call interfaces, multi-step planning and control flow, memory and state, structured output, guardrails and validation, human-in-the-loop checkpoints, and graceful failure/fallback behavior
• Azure OpenAI services experience including LLM APIs and Azure AI Search, plus associated Azure infrastructure (Azure AI Foundry or AWS Bedrock experience also applies)
• Agent and LLM orchestration frameworks such as LangChain, LangGraph, or similar
• MCP (Model Context Protocol) or other tool-calling/function-calling patterns for LLM-to-system integrations
• Knowledge of AI-specific failure and risk modes and mitigation techniques, including hallucination, prompt injection, data leakage, non-determinism, and runaway tool loops
• Judgment on when an LLM/agent is appropriate and how to bound and validate its output
• Daily working fluency with agentic coding tools (Claude Code is the standard) including Claude Code, Cursor, GitHub Copilot, Devin, or equivalent
• Ability to articulate where agentic coding tools accelerate delivery, where they do not, and how to review and test model-generated code responsibly
• Ability to describe measurable impact on throughput and quality
• Cloud environments experience (Azure preferred) including compute, storage, networking, and IAM fundamentals
• Cloud security fundamentals including identity and access management, secrets management, and network boundaries
• Agile/scrum experience including sprint ceremonies, story estimation, and backlog grooming
• Angular (or comparable modern front-end framework) working knowledge
• Clear written and verbal communication with technical and non-technical partners, comfortable with ambiguity and able to move from business need to scoped, specified, shippable increments

Technologies

• C#/.NET, Java, Node.js/TypeScript, Python
• REST APIs
• Azure Functions, Container Apps
• Azure OpenAI, Azure AI Search, Azure AI Foundry, AWS Bedrock
• LangChain, LangGraph
• MCP (Model Context Protocol)
• Claude Code, Cursor, GitHub Copilot, Devin
• Angular
• Ragas, Langfuse
• Azure Monitor, Application Insights
• CI/CD pipelines
• Infrastructure as code
• Bicep, Terraform, ARM
• RAG where warranted

Benefits

• Bonus opportunities
• Career advancement opportunities at every level
• 401k with company match
• Employee Stock Purchase Plan
• Referral Bonus Program
• Medical, Dental, Vision, Life, and other Insurance Plans (subject to eligibility criteria)
• Paid vacation and sick time for eligible associates
• Paid holidays plus a personal holiday
• Paid Volunteer Time Off that starts on Day 1

Education

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent professional experience
• Demonstrated capability is weighted above credentials

Work Environment

• Hybrid position based at Atlanta, GA headquarters (onsite)
• Standard office schedule Monday through Friday during core business hours
• Collaborative, open-plan office environment within the IT department with dedicated space for focused engineering work
• Regular in-person collaboration with product managers, business stakeholders, and the engineering team
• Occasional visits to retail store locations may be required to gather associate feedback and observe how the product is used in context
• Some extended hours may be needed around major releases or on-call rotations for production incidents
• Standard physical requirements of a professional office environment apply (prolonged sitting, use of a computer workstation, and participation in in-person and video meetings)

Working Conditions (Travel & Environment)

• Travel may be required including air and car travel
• Noise level typically quiet to moderate

Physical/Sensory Requirements

• Sedentary work involving sitting most of the time with brief periods of walking or standing
• Ability to exert 10-20 pounds of force occasionally, and/or negligible amount of force frequently to lift, carry, push, pull, or otherwise move objects

Nice to Have

• Spec-driven development, including using specs to drive AI-assisted implementation
• Building internal developer platforms, frameworks, or SDKs consumed by other engineering teams
• Building or publishing MCP servers, not just consuming them
• LLM and agent evaluation frameworks such as Ragas, G-Eval, LLM-as-judge, or agent trajectory evaluation
• AI observability tooling such as Langfuse (our platform) or equivalent tracing and evaluation systems
• Retrieval infrastructure beyond Azure AI Search (pgvector, Pinecone, Elastic, or hybrid search design)
• Multi-agent orchestration, agent-to-agent protocols, or durable-long-running workflow engines
• Infrastructure as code (Bicep, Terraform, ARM)
• Fine-tuning and model adaptation (LoRA/PEFT, distillation, or evaluating fine-tuning against prompting and retrieval alternatives)
• LLMOps tooling such as MLflow, Weights & Biases, or Azure ML
• Retail systems familiarity (POS, OMS, inventory/merchandising platforms)
• Center of Excellence or innovation-team experience within a larger enterprise
• Open source contributions, technical writing, or speaking in the AI engineering space