Job Description

Summary

As a Machine Learning Engineer, you will design and build AI/ML systems — from classical predictive models to LLM-powered agentic applications — that drive meaningful business outcomes at scale. You will take solutions from research and experimentation through to robust, production-grade deployment, with a growing focus on autonomous agents that reason, plan, use tools, and act on behalf of users and business processes. You will work cross-functionally to identify high-value agentic use cases and turn them into reliable, safe, and measurable products.

Description

The MLOps Engineer will drive end-to-end quality initiatives across data ingestion, model training, deployment pipelines, and MLOps tooling. This hire will build, deploy, and optimize AI/ML based applications with a strong emphasis on scalable, and production-ready systems. You will establish standard methodologies for model integration, deployment, and monitoring using CI/CD principles.

Preferred Qualifications

Familiarity with MLOps practices including model versioning, CI/CD pipelines, and experiment tracking tools such as MLflow or similar

Minimum Qualifications

4+ years of related experience building high-throughput, scalable applications or machine learning models in a production environment

Proficiency in Python with hands-on experience building distributed systems

Experience building large-scale machine learning systems using big data technologies such as Spark, SQL, Snowflake, or similar platforms

Solid understanding of ML fundamentals including supervised/unsupervised learning, model evaluation, and feature engineering

Experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, or Hugging Face Transformers

Experience building and deploying applications on large language models (e.g., Claude, GPT, Gemini, Llama, or other open-source models) via APIs or self-hosted inference (e.g., vLLM, TGI)

Hands-on experience building agentic systems — tool/function calling, multi-step reasoning, memory, and RAG — with frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, or similar

Experience evaluating LLM and agent outputs, including quality assessment, hallucination detection, task-success metrics, and latency benchmarking in production

Strong problem-solving skills with the ability to translate ambiguous business problems into well-defined ML and agentic solutions

Excellent cross-functional communication skills with the ability to collaborate effectively across engineering and data science teams