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Senior Open Source Developer Jobs in Houston, TX

NAVA Software Solutions

Sr AI Agentic Engineer

Spring, TX · On-site

$93K - $127K/yr

Senior AI Agentic Engineer Spring TX - 3 days in office (Tues/Wed/Thurs) Duration: Full time ... Evaluate and select frontier and open-source LLMs (e.g., GPT-4o, Claude, Llama, Mistral, Gemini ...

Microsoft

Senior Software Engineer

Houston, TX · On-site

$117K - $154K/yr

We work in collaboration with Microsoft product teams, partners, and open-source communities to ... We are hiring a Senior Software Engineer with deep experience and expertise in designing and ...

Hp

Strategist, Strategic Technology Accelerator

Spring, TX · On-site

$166K - $265K/yr

Own technical engagement with university labs, open-source communities, research institutions, and ... senior engineers. * Experience structuring and managing technical partnerships with external ...

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Senior Open Source Developer information

See Houston, TX salary details

$23.9K

$76.7K

$156.1K

How much do senior open source developer jobs pay per year?

As of Aug 22, 2026, the average yearly pay for senior open source developer in Houston, TX is $76,672.00, according to ZipRecruiter salary data. Most workers in this role earn between $39,600.00 and $98,400.00 per year, depending on experience, location, and employer.

What is the difference between Senior Open Source Developer vs Open Source Software Engineer?

AspectSenior Open Source DeveloperOpen Source Software Engineer
CredentialsExperience in open source projects, coding skills, possibly certifications in relevant technologiesSimilar credentials, often with additional focus on software engineering principles and certifications
Work EnvironmentCollaborates in open source communities, contributes to projects, often remoteWorks within organizations on open source projects or internal tools, may be part of a team
Industry UsageCommon in tech, software, and open source communitiesUsed in tech companies, startups, and organizations leveraging open source solutions

While both roles involve working with open source projects, a Senior Open Source Developer typically focuses on contributing code and leading community efforts, whereas an Open Source Software Engineer may work within a company to develop, maintain, and integrate open source solutions into products.

What are the most commonly searched types of Open Source Developer jobs in Houston, TX?

The most popular types of Open Source Developer jobs in Houston, TX are:

What are popular job titles related to Senior Open Source Developer jobs in Houston, TX?

For Senior Open Source Developer jobs in Houston, TX, the most frequently searched job titles are:

What job categories do people searching Senior Open Source Developer jobs in Houston, TX look for?

The top searched job categories for Senior Open Source Developer jobs in Houston, TX are:

What cities near Houston, TX are hiring for Senior Open Source Developer jobs?

Cities near Houston, TX with the most Senior Open Source Developer job openings:

Senior Open Source Developer jobs near you
Infographic showing various Senior Open Source Developer job openings in Houston, TX as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 14% Part Time, and 2% Contract. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution, with an average salary of $76,672 per year, or $36.9 per hour.

Sr AI Agentic Engineer

NAVA Software Solutions

Spring, TX • On-site

$93K - $127K/yr

Full-time

Posted 17 days ago

Job description

NAVA Software solutions is looking for a Sr AI Agentic Engineer
Details:
Senior AI Agentic Engineer
Spring TX - 3 days in office (Tues/Wed/Thurs)
Duration: Full time /Direct Hire
The Senior AI Agentic Engineer designs, builds, and operationalizes intelligent agent systems that automate complex enterprise business processes end-to-end. This role works at the intersection of LLMs, systems engineering, and applied machine learning - architecting multi-agent pipelines, tool-augmented reasoning systems, and retrieval-augmented generation (RAG) workflows across a range of enterprise platforms (e.g., Databricks AgentBricks, Azure OpenAI) and open-source frameworks (e.g., LangChain, LangGraph, AutoGen) - with the expectation that the right candidate brings familiarity with the broader and rapidly evolving ecosystem. The ideal candidate brings deep hands-on engineering experience with a proven track record of delivering agentic AI systems into production at enterprise scale - not just prototypes - applying rigorous software engineering principles including modular system design, testability, resilience engineering, and security-by-design to ensure agents are maintainable, reliable, and safe in the long run. This means architecting for failure - building in retries, fallbacks, and graceful degradation - and treating latency and cost as first-class engineering constraints from day one, not afterthoughts discovered in production. Beyond technical delivery, the Senior AI Agentic Engineer mentors engineers across the team, shapes the organization's AI automation strategy, translates ambiguous business problems into well-structured agentic solutions, and drives the responsible and secure deployment of AI agents across business-critical functions.
Job Duties/Roles
Agentic AI System Design & Development
  • Design, build, and deploy end-to-end agentic AI systems using LLMs, tools, memory, and planning frameworks to automate complex, multi-step enterprise business processes.
  • Architect and implement both single-agent and multi-agent workflows for autonomous task execution, decision support, and orchestration - defining agent roles, memory strategies, tool integrations, and handoff protocols.
  • Develop tool-using agents with function calling, structured outputs, API integrations, database connectors, RPA hooks, and enterprise workflow triggers.
  • Lead the integration of agentic solutions with enterprise systems including ERP, Business apps and orchestration platforms such as Databricks, Airflow, and Azure Data Factory.

Retrieval-Augmented Generation (RAG)
  • Design and optimize RAG pipelines including document ingestion, chunking strategies, embedding models, vector store selection, and retrieval ranking for enterprise knowledge bases.
  • Implement advanced retrieval techniques such as hybrid search, metadata filtering, re-ranking, and query rewriting to improve grounding and reduce hallucination.
  • Evaluate and continuously tune RAG systems for accuracy, latency, factual grounding, and cost efficiency.

Model Adaptation & Prompt Engineering
  • Evaluate and select frontier and open-source LLMs (e.g., GPT-4o, Claude, Llama, Mistral, Gemini) and apply fine-tuning strategies - including instruction tuning appropriate to each business use case.
  • Optimize prompts, system instructions, and output schemas for reliability, determinism, and safety across agentic pipelines.
  • Apply reinforcement or feedback-driven optimization where applicable, including human-in-the-loop and automated evaluation loops.

Evaluation, Monitoring & Governance
  • Define evaluation frameworks for agentic systems covering task success, factuality, grounding, latency, cost, and failure mode analysis.
  • Build observability and monitoring pipelines for agent behavior, tool call traces, and runtime failure detection.
  • Partner with governance, risk, and compliance teams to ensure responsible AI practices, audit traceability, data privacy, and regulatory adherence across all deployed agents.

Production Deployment & LLMOps
  • Deploy GenAI and agentic systems into production using cloud-native architectures on platforms such as Azure, AWS Bedrock, or Google Vertex AI with containerized (Docker/Kubernetes) delivery.
  • Implement CI/CD pipelines, prompt versioning, rollback strategies, and runtime safeguards for LLM applications in enterprise environments.
  • Optimize deployed systems for performance, cost efficiency, and scalability under real-world load.

Collaboration, Mentorship & Strategy
  • Collaborate with software engineers, product managers, data scientists, and business stakeholders to translate ambiguous process challenges into well-structured agentic solutions.
  • Mentor AI engineers and data scientists on agentic design patterns, responsible AI practices, and production-grade engineering standards.
  • Contribute to the organization's AI automation strategy, co-authoring technical roadmaps, governance policies, and center-of-excellence standards for agentic AI.
  • Stay at the forefront of the agentic AI landscape, rapidly evaluating new frameworks and research findings and communicating their business relevance to leadership.

Knowledge, Skills and Abilities Required (KSAR)
Technical - Agentic Frameworks & LLMs
  • Proven enterprise experience architecting and deploying production-grade multi-agent AI systems that automate real business workflows end-to-end - not just proofs of concept.
  • Deep hands-on expertise with agent orchestration frameworks such as LangChain, LangGraph, AutoGen, Semantic Kernel, DSPy, CrewAI, and platform-native solutions such as Databricks AgentBricks / Mosaic AI Agent Framework - with openness to emerging tools in the rapidly evolving ecosystem.
  • Deep understanding of LLMs and foundation models (e.g., GPT, Claude, Llama, Mistral, Gemini) including their capabilities, limitations, and appropriate use case fit.
  • Experience with structured outputs, function/tool calling, JSON schema design, and multi-turn agent loop engineering.

Technical - RAG & Data Platforms
  • Strong knowledge of RAG architectures, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector), embedding models, and hybrid retrieval strategies.
  • Hands-on experience with Databricks including Unity Catalog, MLflow, Delta Lake, and Databricks Workflows for end-to-end data and AI pipelines.
  • Experience with database technologies, data lakes, and enterprise data platforms including SQL, cloud storage, and streaming data sources that agents consume at runtime

Technical - Deployment, MLOps & Engineering
  • Strong Python proficiency and experience building production-grade services, APIs, and microservices that support agentic systems.
  • Experience deploying LLM and agent workloads on cloud platforms (e.g., Azure OpenAI Service, AWS Bedrock, Google Vertex AI) with containerized infrastructure (Docker, Kubernetes).
  • Experience implementing LLMOps practices including experiment tracking (e.g., MLflow, W&B), prompt versioning, evaluation harnesses, latency profiling, and CI/CD for AI systems.
  • Experience with enterprise security, data governance, and compliance requirements for AI deployments including PII handling, role-based access control, and audit logging.

Evaluation & Responsible AI
  • Familiarity with LLM evaluation techniques, failure mode analysis, red-teaming, and benchmark construction to maintain quality and trust in production agents.
  • Working knowledge of responsible AI principles including fairness, explainability, safety guardrails, and human oversight mechanisms in agentic deployments.

Leadership & Communication
  • Strong written and verbal communication skills with the ability to explain complex GenAI and agentic concepts clearly to both technical teams and executive stakeholders.
  • Demonstrated ability to lead cross-functional AI projects from discovery through production, aligning engineering, data, product, legal, and business operations teams.
  • Ability to mentor junior team members, establishing engineering standards and fostering a culture of experimentation and responsible AI development.
  • Ability to translate ambiguous, open-ended business challenges into structured agentic solution designs with clear scope and success criteria.
  • Display an entrepreneurial mindset with a bias for practical, high-impact solutions; comfortable operating in ambiguous environments and rapidly evolving technology landscapes.

Minimum years of Experience
3 years' work experience as an AI Agentic Engineer and over 7 years' experience in Data Science, Gen AI, Information Systems, Computer Science, Software Engineering or other relevant field with relevant experience.
Required/Preferred Education Requirements
Preferred - Master's Degree in Data Science, Data Analytics, Information Systems, Computer Science, Engineering or other relevant field. Required - bachelor's degree in data science, Information Systems, Computer Science, Engineering or other relevant field with relevant experience.
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About NAVA Software Solutions

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NAVA is a strategic partner for companies seeking to develop or customize software and products. Our team of experts leverages cutting-edge technology and deep industry knowledge to provide customized solutions that drive business success. Whether you're looking to improve your operations, increase efficiency, or bring a new product to market, NAVA has the expertise and resources to help you achieve your goals. Trust us to be your partner in software and product development.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Rocky Hill, CT, US

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