Website:
solveitconsultant.com
Job details:
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).
Click on Apply to know more.