Senior AI Engineer
At a glance
Senior AI Engineering role at xponentiate. Bengaluru · full-time.
Pay not stated AI Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build prompt designs for eight-billion-parameter models, covering classification, extraction, reasoning and summarization tasks
- Tune prompts for accuracy, response speed and cost, then evaluate output accuracy, hallucinations and consistency
- Create reusable prompt templates and move successful prompt workflows into fine-tuned, domain-specific small language models
- Build instruction-tuning and supervised fine-tuning pipelines, with evaluation datasets and automated benchmarks
- Implement retrieval-augmented and tool-based workflows, and deploy models in private cloud or on-premise settings
What you bring
- A master’s or doctoral degree in computer science, AI, machine learning or a related field
- Four to six years in machine learning or NLP, including at least three years focused on large language or foundation models
- Hands-on fine-tuning of open-source models, plus experience with LoRA, QLoRA, adapters or distillation
- Strong Python and PyTorch skills, with knowledge of transformers, tokenization, embeddings and attention
Who this fits
This suits a senior engineer focused on prompt systems, fine-tuning and small language model development for enterprise tasks. The role is onsite in Bengaluru.
From the employer
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.