Senior AI/Machine Learning Engineer

We are opening a priority requirement for Senior AI/Machine Learning Engineer . Please note that we need quality over quantity —only share profiles that strictly match the requirements below. Bulk submissions without alignment will not be considered.

Position: Senior AI/Machine Learning Engineer (LLM Fine-Tuning & Training)

Experience: 7+ years in ML/AI engineering with recent (last 8–12 months) hands-on expertise in fine-tuning and large-scale training of LLMs/VLMs

Budget :- 1.6 LPM

Skills:

•⁠ ⁠Strong expertise in PyTorch, Hugging Face Transformers, PEFT (LoRA/QLoRA)

•⁠ ⁠Hands-on experience with distributed training (DDP, FSDP, DeepSpeed, Accelerate)

•⁠ ⁠Familiarity with evaluation frameworks (lm-eval-harness, custom metrics)

Engagement: Full-time Contract (2 months)

Locations: India

Availability: Full-time, with 4 hours overlap in PST timezone

Start Date : Within 1 week

Evaluation Process: : Technical Interview(Flocareer) + Delivery Round

Important Note:

We are looking for active ICs who have directly worked on model fine-tuning/training in the last 8–12 months. Managerial profiles will not be considered.

Kindly prioritize this role and share only qualified profiles with immediate availability at the earliest.

Role Overview:

We are looking for a Senior AI/Machine Learning Engineer (7+ years of experience) who is a stellar individual contributor with recent hands-on expertise in fine-tuning and large-scale training of modern models (LLMs/VLMs).

This is a hands-on role where you will :

- Lead fine-tuning workflows, large-scale training runs, and evaluation design.

- Collaborate closely with researchers to bring cutting-edge approaches from papers into production.

- Work directly with customers to align performance metrics, validation, and deployment readiness.

Note: This is not a managerial role. We are seeking candidates who are currently active ICs, heavily involved in model fine-tuning/training in the last 8–12 months.

What You’ll Do Day-to-Day

•⁠ ⁠Fine-tune and train LLMs/VLMs at scale (LoRA/QLoRA, PEFT methods).

•⁠ ⁠Build reproducible training pipelines with distributed training and mixed precision.

•⁠ ⁠Design and run robust evaluation frameworks (task-specific + lm-eval-harness).

•⁠ ⁠Translate research papers into working implementations, collaborating with researchers.

•⁠ ⁠Work with customers to validate models against business and performance needs.

•⁠ ⁠Optimize training runs with profiling, performance tuning, and efficiency improvements.

•⁠ ⁠Maintain experiment tracking, reproducibility, and structured model artifacts.

Requirements

•⁠ ⁠7+ years in ML/AI engineering, with a strong recent focus on fine-tuning and training large models.

•⁠ ⁠Expert in PyTorch, Hugging Face Transformers, and PEFT (LoRA/QLoRA).

•⁠ ⁠Strong experience with distributed training (DDP, FSDP, DeepSpeed, Accelerate).

•⁠ ⁠Skilled with evaluation frameworks (lm-eval-harness, custom metrics, task benchmarks).

•⁠ ⁠Proven ability to reproduce and improve results from recent research papers.

•⁠ ⁠Strong coding practices in Python, with modular, clean implementations.

•⁠ ⁠Familiarity with experiment tracking tools (Weights & Biases, MLflow).

•⁠ ⁠Ability to interact with customers and researchers to translate requirements into engineering solutions

[Bonus] Preferred Qualifications:

•⁠ ⁠Experience with other ML libraries (e.g., PyTorch, Flax)

•⁠ ⁠Background in ML research or scientific computing

•⁠ ⁠Experience with production model monitoring and governance

Job Type: Full-time

Pay: ₹500,000.00 - ₹1,200,000.00 per year

Work Location: In person