Senior ML Engineer

LMK Infotech
United States
2 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Shift work
Job source

Tech stack

Training Data Artificial Intelligence Cloud Computing Continuous Integration Python (Programming Language) Tensorflow Azure Machine Learning Feature Engineering Pytorch Large Language Models Scikit Learn Machine Learning Operations
+1 more
Software Version Control

Job description

You’ll work directly with clients and cross-functional teams to build models that solve real business problems - demand forecasting, clinical document understanding, risk scoring, and intelligent process automation. Every model you build ships to production and creates measurable impact., * Design and implement end-to-end ML pipelines - ingestion, feature engineering, training, evaluation, and serving

  • Build and fine-tune models for NLP, structured prediction, and time-series forecasting
  • Deploy models to production with monitoring, drift detection, and automated retraining
  • Collaborate with data engineers on feature stores and training data pipelines
  • Evaluate and integrate LLM-based solutions where they provide clear value
  • Establish best practices for experiment tracking, model versioning, and reproducibility

Requirements

Do you have experience in Production systems?, * 5+ years building and deploying ML models in production

  • Strong Python and ML framework experience (PyTorch, TensorFlow, or scikit-learn)
  • Cloud ML platform experience (SageMaker, Vertex AI, or Azure ML)
  • Solid understanding of MLOps - CI/CD for models, monitoring, and serving infrastructure
  • Comfort with messy real-world data and robust preprocessing pipelines
  • Ability to explain model trade-offs to non-technical stakeholders

Benefits & conditions

Pulled from the full job description

  • Work from home stipend
  • Vision insurance
  • Dental insurance
  • Unlimited paid time off
  • Conference stipend
  • Flexible schedule, * Experience fine-tuning and deploying LLMs in production
  • Background in healthcare, finance, or regulated industries
  • Experience building RAG systems
  • Contributions to open-source ML projects

What we offer

  • Remote-first with async collaboration and flexible hours
  • Competitive salary with equity
  • Unlimited PTO
  • Learning and conference budget
  • Home office stipend
  • Health, dental, and vision coverage

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on indeed.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

1:42 min

Choosing specialized libraries beyond scikit-learn

Adrian Schmitt · LIVE

5:28 min

Defining MLOps and its role in production systems

Hauke Brammer · WWC 2023

1:39 min

Fundamentals of tensors and the TensorFlow library

Håkan Silfvernagel · LIVE

2:35 min

Preventing remote code execution in PyTorch models

Balázs Kiss · WWC 2023

1:43 min

Leveraging modern frameworks across the machine learning lifecycle

Daniel Graff +1 · WWC 2021

3:14 min

Structuring career paths and localized data architectures

Ulrich Wurstbauer +1 · LIVE

Videos

See all

Related articles

See all