Senior AI Engineer

Location: Bengaluru

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 Contiinex’s AI differentiation—building 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

• 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 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, 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