MLOps Engineer

InfoVision Inc.; Irving (TX); On-site; USD 100,000 - 130,000; Full time

Job summary

InfoVision Inc. is seeking an MLOps Engineer to productionize and scale machine learning and Generative AI systems. The successful candidate will focus on LLM deployment, orchestration, and reliability in production environments.

This role involves deploying and managing ML/DL models, building Kubernetes-based infrastructures, and designing scalable inference systems. Strong experience with model deployment, optimization of LLMs, and proficiency in Python are essential.

Qualifications

• Experience with deploying, managing, and scaling ML/DL models in production.
• Hands-on experience with Kubernetes-based infrastructure for ML workloads.
• Proficiency in model packaging, serialization, and versioning.

Responsibilities

• Deploy, manage, and scale ML/DL models in production.
• Build and operate Kubernetes-based infrastructure for ML workloads.
• Design scalable inference systems for batch and real-time processing.

Skills

Hands-on experience with ML/DL models and serialization

Proven experience in model deployment, scaling, and monitoring

Experience with local LLM deployment and optimization

Solid understanding of LLM memory patterns

Experience with API gateways

Familiarity with GenAI workflows

Experience building agentic systems

Proficiency in Python

Job description

MLOps Engineer to productionize and scale ML and GenAI systems, with a focus on LLM deployment, orchestration, and reliability in production environments.

Key Responsibilities

Deploy, manage, and scale ML/DL models in production

Build and operate Kubernetes-based infrastructure for ML workloads

Handle model packaging, serialization, and versioning

Design scalable inference systems (batch and real-time)

Deploy and optimize local LLMs (latency, throughput, cost)

Build and manage agentic systems with tool integration

Design and manage LLM memory (short-term, long-term, vector stores)

Integrate and manage API gateways for model access, routing, and rate limiting

Monitor performance, drift, and system reliability

Requirements

Hands-on experience with ML/DL models and serialization

Proven experience in model deployment, scaling, and monitoring

Experience with local LLM deployment and optimization

Solid understanding of LLM memory patterns (context windows, retrieval, persistence)

Experience with API gateways, load balancing, and service routing

Familiarity with GenAI workflows (RAG, orchestration frameworks)

Experience building agentic / multi-step LLM systems

Proficiency in Python and modern ML/infra tooling