Senior AI/ML Engineer

Job requirements

Experience Range:

2–4 years of experience, including at least 2 years specifically building LLM-based applications, RAG systems, or AI agent workflows

Key Responsibilities:

Design and build end-to-end AI agent workflows, from initial prompt design through production deployment, ensuring scalable and robust solutions

Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, including chunking strategies, embedding models, retrieval ranking, and context window management to maximize information accuracy and retrieval efficiency

Build and systematically iterate on prompt engineering layers, testing and refining prompts and chain-of-thought strategies to achieve consistent, high-quality outputs across diverse inputs

Implement tool orchestration within agent workflows by integrating agents with databases, rule engines, validation systems, and formatting tools to automate complex tasks

Establish automated quality checks and validation layers to proactively catch issues and ensure high output reliability before human review

Instrument solutions for measurement, collaborating with data scientists to develop evaluation frameworks and track solution performance against defined targets

Deploy and maintain AI/ML solutions in production environments, focusing on reliability, monitoring, and edge case handling

Design and implement feedback loops that capture expert review data and translate it into measurable improvements in agent performance

Required Skills:

Advanced proficiency in Python

Hands-on experience with LLM frameworks such as LangChain, LlamaIndex, or similar

Expertise in prompt engineering and systematic prompt testing

Deep understanding of RAG architectures, including embedding models, vector stores, retrieval strategies, and re-ranking

Experience building multi-step agent workflows with tool use, branching logic, and robust error handling

Experience with production deployment and monitoring of AI/ML solutions

Experience with data pipeline tools and frameworks (KubeFlow, BentoML, Great Expectations, Evidently AI)

Preferred Skills:

Experience with multi-agent orchestration frameworks

Background in content generation, translation, or document processing solutions

Familiarity with fine-tuning LLMs or training reward models

Experience implementing feedback loops or RLHF mechanisms

Expertise in LLM cost optimization strategies such as model routing, caching, and prompt compression

Experience with multi-modal AI systems including voice-to-text, document understanding, and image analysis

Experience with evaluation frameworks for generative AI, including automated scoring and human evaluation protocols

Desired Qualifications:

Bachelor's degree in Computer Science, Data Science, Information Technology, or a closely related discipline

Certification in Machine Learning or Artificial Intelligence (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning Specialty)

Certification in LLM or generative AI technologies (e.g., OpenAI Certified Engineer, Hugging Face Certified AI Practitioner)