Senior AI Engineer

2070Health; Bengaluru; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

A leading AI enterprise in Bengaluru is seeking a Senior AI Engineer to design and optimize domain-specialized language models. The role focuses on prompt engineering for 8B-class models, developing evaluation frameworks, and fine-tuning models for enterprise applications. Candidates should possess a Master’s degree or PhD in relevant fields and have 4–6 years of experience in machine learning or NLP, specifically with LLMs. Proficiency in Python and experience with tools like Docker and Kubernetes are essential.

Qualifications

• Strong expertise in designing prompts for 7B–13B models.
• 4–6 years of experience in machine learning with 3+ years on LLMs.
• Experience with fine-tuning open-source models like LLaMA and Mistral.

Responsibilities

• Design production-grade prompt architectures for 8B-class models.
• Develop structured prompts for enterprise tasks.
• Build evaluation frameworks to measure accuracy and consistency.

Skills

Prompt Engineering

LLM fine-tuning

Machine Learning/NLP

Python programming

Education

Master’s degree or PhD in Computer Science, AI, or Machine Learning

Tools

PyTorch

Docker

Kubernetes

Job description

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, optimise, 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 summarisation.
• Optimise 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) optimised 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, tokenisation, 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.