Role Overview

We are looking for an AI Engineer to join our AI team and help design, build, deploy, and improve production-oriented AI solutions across internal operations and customer-facing products. This role is ideal for someone who combines strong software engineering fundamentals with practical experience in modern AI systems and enjoys turning ideas into reliable business outcomes. You will work closely with AI engineers, business stakeholders, and domain experts to build solutions that leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), workflow automation, and agentic AI systems. Our environment emphasizes fast execution, disciplined engineering, continuous learning, and a strong focus on quality, reliability, and measurable impact.

Key Responsibilities

Design, build, and maintain AI-powered applications for internal and customer-facing use cases.
Develop and optimize RAG pipelines using enterprise documents, structured data, and domain-specific knowledge sources.
Build agentic workflows, AI copilots, and automation systems that support decision-making, task execution, and productivity improvement.
Develop backend services, APIs, and integration layers that enable AI capabilities across business applications.
Collaborate with stakeholders to understand business requirements and translate them into practical AI solutions.
Evaluate and improve model performance, retrieval quality, prompt effectiveness, response accuracy, latency, and cost efficiency.
Contribute to converting AI prototypes and proof-of-concepts into scalable, production-ready solutions.
Implement observability, monitoring, logging, evaluation, and guardrails for deployed AI systems.
Support integration with enterprise systems, databases, reporting platforms, and operational workflows.
Document architecture decisions, implementation details, experiments, and operational processes.
Stay current with emerging AI tools, frameworks, and techniques, and proactively explore better approaches for solving business problems.

Requirements

Strong Python programming skills and solid software engineering fundamentals.
Experience building backend services and APIs using FastAPI, Flask, or similar frameworks.
Good understanding of LLM application development, including prompt engineering, tool calling, structured outputs, and response validation.
Hands-on experience with RAG systems, including embeddings, chunking, retrieval strategies, reranking, context management, and evaluation.
Familiarity with AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or equivalent.
Experience with vector databases or semantic search technologies such as pgvector, Pinecone, Weaviate, Milvus, or similar platforms.
Working knowledge of SQL, REST APIs, JSON, and enterprise integration patterns.
Basic familiarity with Oracle or enterprise database environments and an understanding of how AI systems connect to structured business data.
Experience with Docker, Git, CI/CD workflows, and deployment practices.
Strong debugging, analytical thinking, experimentation, and problem-solving skills.
Ability to write clean, maintainable, and production-quality code.

Preferred / Nice to Have

Experience with fine-tuning, LoRA/QLoRA, synthetic data generation, or domain adaptation workflows.
Exposure to LLMOps, MLOps, model evaluation frameworks, observability platforms, and AI guardrails.
Experience deploying or working with self-hosted and open-source models.
Familiarity with OCR, document processing, multimodal AI systems, or enterprise search solutions.
Exposure to workflow automation platforms and enterprise productivity tooling.
Experience with Kubernetes, cloud platforms, model serving, or scalable AI infrastructure.
Familiarity with Jira, Confluence, Zendesk, Power BI, or similar enterprise tools.
Exposure to manufacturing, quality systems, MRO, aerospace, defense, or other enterprise software environments.