Machine Learning Engineer

We’re looking for an experienced AI/ML Engineer to design, build, and deploy production-ready AI and machine learning solutions. This role is highly hands-on and focused on taking AI capabilities from concept through production, including generative AI, LLM-powered applications, intelligent automation, and traditional machine learning.

The ideal candidate combines strong software engineering fundamentals with practical experience building AI systems that solve real business problems.

What You’ll Do

Design, build, test, and deploy production-grade AI/ML applications and services

Develop applications using large language models (LLMs), generative AI, and modern ML techniques

Build RAG pipelines, AI agents, workflow automation, and intelligent search/retrieval solutions

Integrate commercial and open-source models into enterprise applications

Develop and optimize prompts, context management, embeddings, vector search, and model orchestration

Build APIs and backend services that expose AI/ML capabilities to applications and internal systems

Evaluate model and application performance for accuracy, reliability, latency, and cost

Design data pipelines supporting model training, inference, retrieval, and evaluation

Implement appropriate guardrails, monitoring, observability, and security controls for production AI

Work closely with engineering, product, data, and business stakeholders to identify high-value AI use cases

Prototype quickly while maintaining a clear path from proof of concept to scalable production systems

Stay current with rapidly evolving AI models, frameworks, architectures, and development practices

What We’re Looking For

5+ years of professional software engineering, machine learning engineering, or related experience

Strong programming skills in Python

Hands‑on experience building and deploying AI/ML systems in production

Experience working with LLMs and APIs from providers such as OpenAI, Anthropic, Google, or comparable open‑source models

Experience with RAG, embeddings, vector databases, prompt engineering, and LLM evaluation

Strong understanding of APIs, microservices, distributed systems, and modern application architecture

Experience with ML/AI frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, or similar technologies

Experience deploying AI/ML workloads in AWS, Azure, or GCP

Familiarity with Docker, CI/CD, Git, and modern DevOps/MLOps practices

Experience working with structured and unstructured data

Strong problem‑solving skills and the ability to translate ambiguous business problems into practical technical solutions

Nice to Have

Experience building agentic AI systems and multi‑step AI

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