AI Focused Software Developer - Python

Univedge Consulting LLC; Palo Alto (CA); Hybrid; USD 210,000 - 290,000; Full time

Job summary

Univedge Consulting LLC in Palo Alto is seeking an Applied AI Developer on the Enterprise AI team. You will evaluate where AI fits, build the tools and platforms that enable practical AI adoption, and support teams in deploying agentic workflows and governance.

You stay hands-on with prototyping, evaluating emerging AI tech, and shipping scalable solutions. You will own end-to-end delivery—from concept to production—across client engagements, collaborating with product, security, and platform

Qualifications

• 10+ years of professional software engineering experience, with at least 2 years in applied AI in production systems.
• Proficient in Python and/or Go; comfortable with the other language.
• Proven experience building and scaling multi-agent or agent-driven systems in production.
• Hands-on with modern agent ecosystems and leading AI SDKs (OpenAI, Anthropic).
• Strong systems and backend architecture fundamentals; scalable, reliable deployments.
• Experience with cloud-native environments (GCP/AWS) and enterprise APIs (REST/GraphQL).

Responsibilities

• Think AI-first: build automated evals, simulation tests, and regression frameworks to keep AI systems reliable as they scale.
• Design agentic systems: tool orchestration, agent reasoning, memory, MCP integrations, and human-in-the-loop workflows.
• Define and implement AI governance patterns: guardrails, data lineage, auditability, safe and compliant systems.
• Drive adoption through pilots, proofs-of-concept, and scalable implementations across engineering teams.
• Collaborate with Product, Operations, and domain teams to translate AI use cases into production-grade solutions.

Skills

Python

Go

Agent systems

LLM orchestration

Observability tooling

DevOps

REST/GraphQL

RBAC

Tools

LangGraph

Google ADK

Mas tra

Claude Agent SDK

Langfuse

LangSmith

Braintrust

MCP implementations

OpenAI

Anthropic

Docker

Kubernetes

Job description

Palo Alto, CA (Hybrid - 3 days onsite per week)

A minimum of 10 years of relevant experience

About the Role:

As an Applied AI Developer on the Enterprise AI team, you'll evaluate where AI fits, build the tools and platforms that make it practical, and enable teams across client to adopt modern AI development patterns - including LLM orchestration, agentic workflows, and model governance. You stay hands-on and outcomes-oriented: prototyping, evaluating emerging AI technologies, and shipping solutions that make the organization measurably more efficient.

What You'll Do:

• Think AI-first - assess where agentic approaches genuinely outperform conventional solutions, then own the quality bar: build automated evals, simulation tests, and regression frameworks that keep our AI systems reliable and improving as they scale.
• Design agentic systems - tool orchestration, agent reasoning, memory, MCP integrations, and human-in-the-loop workflows.
• Define and implement AI governance patterns - guardrails, data lineage, auditability, and responsible AI practices that ensure our agentic systems are safe, compliant, and trustworthy.
• Drive adoption through pilots, proofs-of-concept, and scalable implementations across engineering teams.
• Collaborate with various business functions, product, security, and platform teams to translate AI use cases into production-grade, end-to-end solutions.

What We're Looking For:

• 5+ years of professional software engineering, with at least 2 years focused on applied AI in production systems.
• Proficient in Python and/or Go; comfortable reading and writing in the other.
• Proven experience building and scaling multi-agent or agent-driven systems in production - real-world operational ownership, not just simple LLM workflows.
• Hands-on experience with modern agent ecosystems, including frameworks (e.g., LangGraph, Google ADK, Mastra, Claude Agent SDK), observability and evals tooling (e.g., Langfuse, LangSmith, Braintrust), MCP implementations, and leading AI SDKs (e.g., OpenAI, Anthropic).
• Strong systems and backend architecture fundamentals - designing scalable, reliable systems and handling infrastructure, performance, failure modes, cost, and deployment concerns.
• Good understanding of cloud-native environments (GCP and/or AWS) - compute, storage, networking, and managed AI services.
• Experience designing and integrating with enterprise APIs (REST, GraphQL) including authentication and authorization patterns (OAuth2, SAML, API keys, RBAC). Comfortable working with backend databases (SQL and NoSQL) - writing queries, understanding data models, and building data access layers that enforce role-based access control.
• Strong cross-functional collaborator and communicator, able to partner with Product, Operations, and domain experts to deliver end-to-end systems with measurable real-world impact.
• A force-multiplier on the team - you raise the bar for clarity of thinking, system design standards, and team execution.

Nice to Have:

• Experience with AI evaluation tooling (Langfuse, LangSmith, Braintrust, or custom eval frameworks).
• Experience building custom MCP servers, not just consuming them.
• Familiarity with containerization and orchestration (Docker, Kubernetes).
• AI-native builder with high velocity and ownership - intellectual curiosity, rapid adoption of new tools, bias to action, and the ability to drive ambiguous problems from concept to production.
• Hands-on experience with inference cost optimization - managing spend as agent deployments scale.
• Experience using AI-powered coding agents (e.g., Claude Code, GitHub Copilot, Cursor, Windsurf) to accelerate development workflows - rapid prototyping, code generation, debugging, and test writing.
• Experience with RAG (Retrieval-Augmented Generation) architectures and document retrieval pipelines - vector databases, embedding models, chunking strategies, and hybrid search - for building agents that answer questions grounded in enterprise documentation.