Intellectsoft is a software development company delivering creative solutions since 2007. Our client is a management consulting and technology firm focused on transforming global healthcare and diverse industries through data-driven strategies, cutting-edge analytics, and advanced AI solutions. This role sits within a focused research initiative dedicated to adapting small language models (SLMs) to specialized, high-value domain applications.
Experience: 3+ years in Machine Learning Engineering or Applied ML, with hands-on experience fine-tuning transformer-based language models.
Core Tech Stack: Advanced Python and PyTorch proficiency.
Strong understanding of base model architectures, tokenizer design, pre-training data distributions, licensing, and context window requirements.
Data Engineering for ML: Proven experience building training pipelines (curation, cleaning, synthetic data generation, and formatting).
Ability to design executable correctness evaluations, paired with hands-on experience serving and quantizing models using at least one modern inference framework.
Research Fluency: Comfort reading recent ML research papers and translating state-of-the-art methods into working code.

Hands-on experience with Continued Pre-Training (CPT) on large domain corpora.
Experience with Reinforcement Learning / preference optimization for LLMs (reward modeling, DPO, GRPO,



or verifiable-reward setups).
Experience with structured-output generation, code generation (e.g., SQL/DDL), or domain classification fine-tuning.
Familiarity with distributed training frameworks (DeepSpeed, FSDP).
Knowledge distillation experience transferring capabilities from large teacher models to small student models.
Experience with on-device or edge deployment.
Active open-source contributions to machine learning tooling, libraries, or published models.
Drive end-to-end adaptation of SLMs to specialized domain tasks, choosing the right base models, training strategies, and architectures.
Select Strategy per Task: Determine and sequence optimal adaptation strategies - including Continued Pre-Training (CPT), Supervised Fine-Tuning (SFT), and RL/preference tuning (DPO, GRPO, RLHF).
Curate & Synthesize Data: Clean, curate, format, and generate high-quality datasets, including synthetic data generation when domain data is scarce.
cpp, or TensorRT-LLM.
Pipeline Reproducibility & Research Execution: Break target tasks into milestones with clear criteria, maintaining reproducible pipelines with data/config versioning, checkpointing, and experiment tracking.
Paid PTO policy (vacation, sick leaves, and public holidays)
Flexible hours & work setup

📌 Machine Learning Research Engineer (Bengaluru)
🏢 Intellectsoft
📍 Bengaluru