Senior AI Engineer

Contiinex is an AI-first enterprise automation platform for healthcare and insurance, purpose-built to understand unstructured conversations, documents, and workflows, and autonomously execute complex, human-intensive business processes.

We build specialised domain-trained Small Language Models (SLMs) and fine-tuned LLM pipelines designed to operate reliably in regulated, high-stakes environments such as US Healthcare Revenue Cycle Management (RCM).

Our architecture emphasizes deterministic AI systems combining prompt engineering, model fine-tuning, and agentic orchestration to power real enterprise automation.

We are seeking a Senior AI Engineer with strong expertise in Prompt Engineering, LLM fine tuning, and Small Language Model (SLM) development to design, train, optimize, and deploy domain-specialised language models.

A key focus of this role will be engineering high-performance prompts for 8B-class models (such as LLaMA, Mistral, and Qwen) and transitioning these prompts into fine-tuned models for production reliability.

You will design prompt architectures, instruction schemas, and evaluation pipelines that ensure models produce accurate, structured, and deterministic outputs suitable for enterprise automation workflows.

● Design production-grade prompt architectures for 8B-class models.

● Develop structured prompts for enterprise tasks such as classification, extraction, reasoning, and summarization.

● Optimize prompts for accuracy, latency, and cost efficiency.

● Build prompt evaluation frameworks to measure accuracy, hallucination rates, and consistency.

● Design reusable prompt libraries and prompt templates for enterprise workflows.

● Develop prompt-to-model migration strategies converting high-performing prompts into fine-tuned SLMs.

● Design and fine-tune LLMs for domain-specific enterprise tasks.

● Develop Small Language Models (SLMs) optimized for enterprise deployment.

● Build instruction tuning and supervised fine-tuning (SFT) pipelines.

● Design evaluation datasets and automated benchmarking frameworks.

● Implement retrieval augmented generation (RAG) pipelines and tool-augmented workflows.

● Collaborate with speech AI and document AI teams to build multimodal systems.

● Deploy models in private cloud or on-premise environments with strong security controls.

Master’s degree or PhD in Computer Science, AI, Machine Learning, or a related field.

● Strong expertise in Prompt Engineering for 7B–13B models (especially 8B models).

● Experience designing prompts for structured enterprise outputs.

● Experience building prompt evaluation datasets and benchmarking frameworks.

● Ability to convert prompt workflows into fine-tuned models.

● 4–6 years of experience in ML/NLP with 3+ years focused on LLMs or foundation models.

● Hands-on experience fine-tuning open-source models such as LLaMA, Mistral, Falcon, or Qwen.

● Experience with LoRA, QLoRA, adapters, and model distillation techniques.

● Strong understanding of transformers, tokenization, embeddings, and attention mechanisms.

● Strong Python engineering skills and experience with PyTorch.

● Experience with GPU-based training and inference.

● Familiarity with Hugging Face, Accelerate, DeepSpeed, and Triton.

● Experience with vector databases and RAG architectures.

● Experience deploying models using Docker, Kubernetes, and cloud platforms. Compliance & Enterprise Readiness

● Experience working in regulated environments.

● Understanding of data privacy, access controls, and AI auditability.

● Ability to design AI guardrails and human-in-the-loop workflows.