Artificial Intelligence Engineer

Location: Bengaluru

Company Overview

An AI-first enterprise automation platform focused on healthcare and insurance, purpose-built to understand unstructured conversations, documents, and workflows, and autonomously execute complex, human-intensive business processes. The platform builds specialised, domain-trained Small Language Models (SLMs) and fine-tuned LLM pipelines that operate reliably in regulated, high-stakes environments such as US Healthcare Revenue Cycle Management (RCM).

Role Overview

We are seeking a Senior AI Engineer with deep expertise in LLM fine-tuning and SLM development to design, train, optimize, and deploy domain-specialised language models. This role is central to building deterministic, production-grade intelligence that powers agentic enterprise workflows—moving beyond generic prompting into reliable, scalable AI systems.

You will work closely with data science, product, speech AI, and platform teams to develop scalable, explainable, and compliant language models tailored to real-world enterprise use cases.

Key Responsibilities

- Design, fine-tune, and evaluate LLMs for domain-specific enterprise tasks
- Develop and train Small Language Models (SLMs) optimized for accuracy, latency, and cost
- Build instruction-tuning, supervised fine-tuning (SFT), and preference-alignment pipelines
- Design prompt-to-model migration strategies for production reliability
- Create evaluation frameworks for accuracy, hallucination control, and business relevance
- Work with structured and unstructured datasets (text, transcripts, documents)
- Implement retrieval-augmented generation (RAG) and tool-augmented model workflows
- Collaborate with speech AI and document AI teams to build multimodal pipelines
- Deploy models in private-cloud or on-prem environments with strict security controls
- Continuously optimize models for inference performance and cost efficiency

Required Qualifications

Education

- Master’s degree or PhD in Computer Science, AI, ML, or a related field

Experience & Technical Skills

- 4–6 years of experience in ML/NLP, with 3+ years focused on LLMs or foundation models
- Hands-on experience fine-tuning open-source LLMs (LLaMA, Mistral, Falcon, etc.)
- Strong experience building and training Small Language Models (SLMs)
- Expertise in PyTorch and modern ML training workflows

- Experience with LoRA, QLoRA, adapters, and model distillation
- Strong understanding of tokenization, embeddings, attention mechanisms, and transformers
- Experience building evaluation datasets and automated model benchmarking
- Practical experience with RAG architectures and vector databases
- Experience deploying models using containers and scalable inference frameworks
- Strong Python engineering skills with production-quality coding standards

AI Platform & Infrastructure

- Experience with GPU-based training and inference
- Familiarity with ML tooling (Hugging Face, Accelerate, Deep Speed, Triton, etc.)

- Experience with experiment tracking and model versioning
- Exposure to Kubernetes, Docker, and cloud platforms (AWS, Azure, or GCP)

Compliance & Enterprise Readiness

- Experience working in data-sensitive or regulated environments
- Understanding of data privacy, access controls, and auditability for AI systems
- Ability to design models with explainability, guardrails, and human-in-the-loop support

Nice to Have

- Experience applying LLMs in healthcare, insurance, or financial domains
- Exposure to speech-to-text or document AI pipelines
- Familiarity with agentic workflows and tool-using LLMs
- Experience optimizing models for edge or low-latency environments

Why Join

- Build specialised language models that power real enterprise automation
- Move beyond demos into production-grade, trusted AI systems
- Work alongside domain experts, AI architects, and platform engineers
- Shape the future of applied AI in regulated industries