Senior AI/ML Model Development Engineer

Role: Senior AI/ML Model Development Engineer

Location: Remote

Type: Fulltime

Position Overview

We are seeking an experienced Senior AI/ML Model Development Engineer to lead core

machine learning model design, training, and algorithmic development to solve complex

enterprise problems. Unlike traditional software application engineering or prompt wrappers, this

role focuses on hands-on model engineering, architecture selection, fine-tuning, unsupervised

learning, reinforcement learning, and custom algorithm design. You will own the full model

lifecycle—from exploratory data analysis and custom feature engineering to deep model

training, evaluation, optimization, and production deployment.

Key Responsibilities

Core Model Development & Algorithmic Engineering

● Algorithm Design & Custom Model Training: Design, build, train, and evaluate

tailored machine learning models using supervised, unsupervised, and reinforcement

learning techniques to address domain-specific challenges.

● Beyond Wrappers & Prompts: Go beyond high-level APIs to perform hands-on model

architecture customization, loss function modification, pre-training, and fine-tuning (e.g.,

LoRA, QLoRA) on custom datasets.

● Unsupervised & Reinforcement Learning: Develop advanced unsupervised models

for pattern discovery, clustering, and anomaly detection, as well as RL/RLHF

frameworks to align model outputs with business objectives.

Model Training, Optimization & MLOps

● Model Training Pipelines: Construct distributed model training pipelines using

frameworks such as PyTorch, TensorFlow, or JAX across high-performance compute

clusters.

● Hyperparameter & Loss Optimization: Systematically perform hyperparameter tuning,

model quantization, pruning, and distillation to achieve target accuracy, low latency, and

compute cost efficiency.

● Evaluation & Ground-Truth Testing: Establish rigor around model validation,

monitoring model drift, bias/hallucination analysis, and building automated benchmark

datasets.

Problem Solving & System Architecture

● Domain-Specific AI Solutions: Analyze multi-dimensional enterprise datasets (speech

transcriptions, unstructured textual data, numerical logs) to formulate mathematical and

statistical AI solutions.

● Productionization: Own the model lifecycle end-to-end, serving models via low-latency

APIs or embedded engines while monitoring live inference performance.

Qualifications & Key Requirements

Experience & Core Competencies

● Overall Experience: 8+ years of software engineering or data science experience, with

at least 4+ years specifically dedicated to core machine learning model training,

deep learning, and algorithm development.

● Core ML & Statistical Mastery: Solid mathematical background (linear algebra,

calculus, probability, optimization theory) with proven experience implementing

supervised, unsupervised, and reinforcement learning techniques.

● Model Building Stack: Expert proficiency in Python and deep learning frameworks

(PyTorch, TensorFlow, JAX, scikit-learn, XGBoost/LightGBM).

● Generative AI & LLM Engineering: Hands-on experience with pre-training or fine-

tuning open-weight foundation models (e.g., Llama, Mistral), context optimization, and

model alignment techniques.

● Data & Feature Engineering: Proficiency with high-volume data tools (Pandas, NumPy,

SQL, PySpark) for feature engineering, text/speech dataset creation, and embedding

generation.

Preferred Qualifications

● Strong knowledge of MLOps platforms (MLflow, Kubeflow, Weights & Biases) for model

tracking and artifact management.

● Hands-on experience with speech processing (STT models/Whisper fine-tuning) or NLP

sequence modeling.

● Experience with GPU optimization frameworks (CUDA, TensorRT, vLLM, DeepSpeed).