Senior AI Engineer

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 Contiinexs AI differentiationbuilding models that move beyond generic prompting into deterministic, production-grade intelligence that powers agentic enterprise workflows.

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

Masters degree or PhD in Computer Science, AI, ML, or a related field

Experience & Technical Skills

46 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 fine-tuning techniques such as LoRA, QLoRA, adapters, and 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 code standards AI Platform & Infrastructure

Experience with GPU-based training and inference

Familiarity with ML tooling (Hugging Face, Accelerate, DeepSpeed, 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