Need: Senior AI/ML Ops Engineer for Austin, TX or Sunnyvale, CA

[Ishita]

Senior AI/ML Ops Engineer

Job Details

Job Title: Senior AI/ML Ops Engineer / AI Software Engineer
Location: Austin, TX or Sunnyvale, CA
Work Arrangement: On-Site
Duration: 12+ Months
Experience Required: 5+ Years
Primary Domain: AI/ML, Agentic AI, AI Infrastructure
Primary Technologies: Python, LLMs, MCP, RAG, PostgreSQL, Snowflake

Position Overview

We are seeking a Senior AI/ML Ops Engineer / AI Software Engineer with strong software engineering fundamentals and significant hands-on experience delivering AI/ML solutions across the full lifecycle.

The ideal candidate will be able to operate across AI infrastructure, application development, data engineering, and AI solution delivery, with particular expertise in Agentic AI, AI Agents, MCP (Model Context Protocol), and RAG (Retrieval-Augmented Generation).

The role requires strong programming skills, database expertise, research curiosity, and the ability to translate ambiguous business problems into practical, production-ready AI solutions.

The candidate will also play an important role in AI enablement and knowledge sharing, including organizing technical sessions and helping engineering teams understand emerging AI capabilities.

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Key Responsibilities

AI Application Development

• Design, develop, test, deploy, and maintain robust software applications with an AI-first and agentic AI approach.
• Integrate AI agents, MCP-based tools, and RAG systems into new and existing applications.
• Build and connect MCP servers and clients to enable AI agents to interact with:
  • Internal Systems
  • APIs
  • Databases
  • Enterprise Tools
  • External Data Sources
• Develop agent-driven workflows and intelligent application features.
• Build scalable and maintainable application architectures.
• Collaborate with engineering teams throughout the complete development lifecycle:
  • Requirements
  • Architecture
  • Design
  • Development
  • Testing
  • Deployment
• Write clean, maintainable, scalable, and production-quality code.

Agentic AI & LLM Solutions

• Design and implement Agentic AI systems and AI agent workflows.
• Work with LLMs and agent orchestration frameworks.
• Develop:
  • Multi-Agent Workflows
  • Autonomous Workflows
  • AI Agent Tool Calling
  • MCP-enabled Agent Systems
• Translate ambiguous business problems into practical agent-driven AI solutions.
• Evaluate where agentic approaches can deliver meaningful business value.
• Proactively identify and propose new AI use cases.

MCP – Model Context Protocol

• Build, configure, and integrate MCP servers and clients.
• Connect AI agents to enterprise tools, applications, databases, and internal systems using MCP.
• Design MCP-based tool access and orchestration patterns.
• Evaluate emerging MCP capabilities and recommend appropriate implementation approaches.
• Troubleshoot MCP integrations and agent/tool communication issues.

RAG & Retrieval Systems

• Design and implement Retrieval-Augmented Generation (RAG) solutions.
• Build and optimize:
  • Embedding Pipelines
  • Vector Stores
  • Retrieval Layers
  • Document/Data Processing Pipelines
  • RAG Orchestration
• Integrate RAG systems with AI agents and enterprise applications.
• Evaluate retrieval quality, relevance, performance, and scalability.
• Design appropriate data pipelines to support AI and retrieval workloads.

Data Engineering & Databases

• Design and manage data models, schemas, and queries.
• Work extensively with:
  • PostgreSQL
  • Snowflake
• Design transactional data models and schemas in PostgreSQL.
• Optimize SQL queries and database performance.
• Design analytical and warehouse workloads using Snowflake.
• Develop data pipelines supporting AI/ML applications.
• Ensure data infrastructure can scale with increasing AI and application workloads.

AI Infrastructure & MLOps

• Design, evaluate, and implement infrastructure supporting:
  • Model Training
  • Fine-Tuning
  • Model Inference
  • AI Agents
  • MCP Server/Tool Orchestration
  • RAG Pipelines
• Work with cloud platforms and AI compute infrastructure.
• Support GPU / compute provisioning for AI workloads.
• Build and maintain MLOps pipelines.
• Evaluate infrastructure requirements for growing AI workloads.
• Optimize infrastructure for performance, scalability, reliability, and cost.
• Assess current environments and recommend architecture improvements.

AI Solution Delivery

• Deliver timely, production-ready AI solutions aligned with business requirements.
• Favor practical agentic AI approaches where they provide measurable value.
• Integrate AI agents, MCP, RAG, and structured data pipelines into enterprise applications.
• Proactively pitch AI-driven solutions to stakeholders.
• Identify opportunities for:
  • Multi-Agent Systems
  • MCP-enabled Tool Access
  • RAG
  • Data-Driven AI Insights
  • Intelligent Automation
• Work with cross-functional teams to move AI concepts from experimentation to production.

Research & AI Tooling

• Continuously research emerging AI technologies, frameworks, and platforms.
• Stay current with:
  • Agentic AI
  • AI Agent Frameworks
  • MCP
  • RAG Architectures
  • Vector Databases
  • AI Infrastructure
  • MLOps
  • Database Technologies
• Evaluate internal and external AI tools for applicability to business initiatives.
• Document technology evaluations, findings, and recommendations.
• Maintain up-to-date knowledge of internal AI platforms and tooling.

Knowledge Sharing & Enablement

• Organize and host weekly AI talks, technical sessions, and knowledge-sharing workshops.
• Share learnings, tooling updates, architecture patterns, and best practices.
• Conduct technical deep dives covering:
  • Agentic AI
  • MCP
  • RAG
  • LLMs
  • AI Infrastructure
  • Data Engineering
• Foster a culture of continuous AI learning across engineering teams.
• Help developers and technical teams adopt emerging AI capabilities.

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Required Technical Skills

AI / LLM

• LLMs
• Generative AI
• Agentic AI
• AI Agents
• Agent Orchestration
• Multi-Agent Systems
• Autonomous Workflows
• AI Tool Calling

MCP

• Model Context Protocol (MCP)
• MCP Servers
• MCP Clients
• Tool Integration
• Agent-to-Tool Communication
• Enterprise System Integration

RAG

• Retrieval-Augmented Generation
• Embeddings
• Vector Stores
• Vector Databases
• Retrieval Pipelines
• RAG Architecture
• Retrieval Optimization

Programming

• Python – Preferred / Strongly Required
• Data Structures
• APIs
• Software Engineering Fundamentals
• Testing
• Version Control

Databases

• PostgreSQL
• Snowflake
• SQL
• Database Schema Design
• Query Optimization
• Transactional Systems
• Data Warehousing
• Analytics
• Performance Tuning

AI Infrastructure / MLOps

• AI Infrastructure
• Cloud Platforms
• GPU / Compute Provisioning
• Model Training Infrastructure
• Model Inference
• Fine-Tuning Infrastructure
• MLOps Pipelines
• Production AI Workloads

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Required Qualifications

• Minimum 5 years of professional software development experience.
• Significant hands-on AI/ML experience.
• Strong Python programming skills.
• Strong software engineering fundamentals.
• Hands-on experience with LLMs and Generative AI.
• Experience with Agentic AI / AI Agents.
• Experience with agent orchestration frameworks such as LangGraph or similar.
• Experience building or integrating MCP servers/clients.
• Strong experience with RAG pipelines.
• Hands-on experience with embeddings and vector stores.
• Strong SQL skills.
• Hands-on PostgreSQL experience.
• Hands-on Snowflake experience.
• Experience with AI infrastructure and MLOps.
• Experience with cloud platforms and compute/GPU provisioning.
• Ability to independently research, evaluate, and document new technologies.
• Strong communication and presentation skills.
• Ability to explain complex AI concepts to varied audiences.
• Self-driven and proactive approach to identifying AI opportunities.

Nice-to-Have Skills

• Experience with traditional / early NLP techniques, including:
  • Rule-Based Systems
  • TF-IDF
  • HMMs
  • N-Gram Models
  • Classic POS Tagging
• Experience with modern deep-learning-based NLP.
• Experience scaling Agentic AI infrastructure in production.
• Experience scaling AI data workloads in production.
• Experience organizing internal technical talks or workshops.
• Experience building AI communities of practice.
• Experience mentoring engineering teams on AI technologies.

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Thanks & Regards
Ishita Bali
Novia Infotech LLC
4421 Avenida Ln, McKinney, TX 75070
Email: ishi...@noviainfotech.com