Senior AI Engineer

Description

• About Contiinex
• 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.

Role Overview

• 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.

Key Responsibilities

• ● 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.

Required Qualifications

Education

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

Experience & Technical Skills

• ● 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.

AI Platform & Infrastructure

• ● 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.