Design, build, deploy, and operate production-grade AI agents and multi-agent systems. Implement RAG, agent orchestration, copilots, observability, memory patterns, and enterprise integrations while ensuring governance, reliability, and onshore/offshore delivery.
Senior Agentic AI Engineer (Python)
Experience: 5–10 years
Location: Onsite / Offshore (Flexible)
Primary Objective
We are seeking a Senior Agentic AI Engineer to design, build, deploy, and operate enterprise-grade AI agents and multi-agent systems. The role focuses on Generative AI, LLMs, agentic workflows, RAG architectures, AI orchestration, and governed enterprise AI platforms—delivering scalable copilots, intelligent automation, and knowledge systems across onshore and offshore delivery environments.
Success looks like: production-ready agents with measurable reliability, governed RAG integrated into enterprise systems, and clear observability/guardrails for business use cases.
Key Responsibilities
Primary
- Design, develop, and deploy AI agents and multi-agent workflows using Python and agentic frameworks (e.g., LangChain, LangGraph, or equivalent).
- Build enterprise-scale RAG solutions over structured and unstructured data sources.
- Develop agent orchestration workflows integrating models, tools, APIs, and enterprise services.
- Build AI-powered copilots, assistants, and automation solutions for enterprise use cases.
- Implement AI monitoring, observability, tracing, telemetry, and performance measurement.
Also expected
- Implement agent memory patterns (short-term, long-term, episodic).
- Integrate agents with enterprise platforms (APIs, databases, SharePoint, Confluence, Salesforce, knowledge repositories).
- Contribute to AI governance: guardrails, security controls, policy enforcement, and compliance.
- Optimize prompts, reasoning strategies, workflows, and execution performance.
- Collaborate with Data Science and ML teams on evaluation, optimization, and continuous improvement.
Must-Have Experience & Skills
- 5–10 years of software engineering experience with strong Python expertise.
- 3+ years designing and implementing AI/ML solutions.
- Hands-on experience building Generative AI applications using LLMs, RAG, and agentic frameworks (LangChain/LangGraph or equivalent; OpenAI SDK or similar).
- Experience delivering enterprise-grade AI solutions in production.
- Experience with distributed onshore/offshore team delivery.
- Solid API/service engineering fundamentals (REST/async services, integration patterns).
- Practical understanding of AI observability, evaluation, and production reliability.
Preferred Skills
- Cloud AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI, or similar).
- Vector databases / search (pgvector, OpenSearch, Pinecone, Weaviate, or similar).
- LLMOps tooling (tracing, eval harnesses, prompt/version management).
- Enterprise integrations (SharePoint, Confluence, Salesforce).
- Containerized deployment practices (Docker; Kubernetes a plus).
- AI security, PII handling, and guardrail frameworks.
Soft Skills
- Clear written and verbal communication with technical and business stakeholders.
- Ability to own delivery end-to-end in a distributed team model.
- Pragmatic trade-off judgment between speed, quality, cost, and governance.
Compensation, Benefits and Duration
Minimum Compensation: USD 48,000
Maximum Compensation: USD 168,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is available for independent contractors
No applications will be considered if received more than 120 days after the date of this post
Similar Jobs
Financial Services
Leads software engineering and AI/ML solution delivery using Java and Python. Designs, builds, operates, and troubleshoots secure, scalable production systems; advances AI-assisted engineering practices; integrates LLM, RAG, and NLP capabilities; develops CI/CD and cloud-native automation; improves observability, reliability, and incident remediation; partners on technical roadmaps; and mentors engineers and AI practitioners.
Top Skills:
Ai/MlAWSCi/CdCloud-Native ArchitecturesContainerizationEvent-Driven ArchitectureGenerative AiInfrastructure As CodeJavaLangchainLanggraphLlmsMicroservicesNlpObservabilityPythonRetrieval-Augmented Generation (Rag)
Aerospace • Hardware • Information Technology • Security • Software • Cybersecurity • Defense
Develop and maintain Master Data Management, data governance, stewardship, quality, and reference data processes. Partner with enterprise stakeholders and governance groups to define standards, validate master data, establish quality rules, support data owners, and prioritize initiatives. Lead requirements for system configuration changes while using data profiling, modeling, and analytics to improve data validity and consistency across systems.
Top Skills:
AgileInformaticaProfiseePythonRestSemarchySoapSQL
Aerospace • Hardware • Information Technology • Security • Software • Cybersecurity • Defense
Develop and maintain master data management and integration capabilities, including data quality rules, governance processes, stewardship workflows, data validation, profiling, modeling, and integration design. Collaborate with enterprise stakeholders, data owners, stewards, and governance groups to define requirements, configure systems, and support strategic MDM initiatives. Use Python, SQL, AWS services, APIs, Dataiku, and ETL/MDM tools to improve data accuracy, consistency, transformation, and retention.
Top Skills:
AgileAmazon RedshiftAPIsAws LambdaAws Step FunctionsDataikuETLInformaticaPl/SqlProfiseePythonSemarchySQL
What you need to know about the Austin Tech Scene
Austin has a diverse and thriving tech ecosystem thanks to home-grown companies like Dell and major campuses for IBM, AMD and Apple. The state’s flagship university, the University of Texas at Austin, is known for its engineering school, and the city is known for its annual South by Southwest tech and media conference. Austin’s tech scene spans many verticals, but it’s particularly known for hardware, including semiconductors, as well as AI, biotechnology and cloud computing. And its food and music scene, low taxes and favorable climate has made the city a destination for tech workers from across the country.
Key Facts About Austin Tech
- Number of Tech Workers: 180,500; 13.7% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Dell, IBM, AMD, Apple, Alphabet
- Key Industries: Artificial intelligence, hardware, cloud computing, software, healthtech
- Funding Landscape: $4.5 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Live Oak Ventures, Austin Ventures, Hinge Capital, Gigafund, KdT Ventures, Next Coast Ventures, Silverton Partners
- Research Centers and Universities: University of Texas, Southwestern University, Texas State University, Center for Complex Quantum Systems, Oden Institute for Computational Engineering and Sciences, Texas Advanced Computing Center
