Senior Machine Learning Engineer

Experience:

Location:

Employment Type:

Compensation:

• Collaborate with business and technology stakeholders to understand current and future ML requirements
• Design, develop, and optimize machine learning models and algorithms for structured and unstructured data
• Build and maintain scalable ML pipelines, architecture, and infrastructure
• Apply statistical and ML techniques such as decision trees, logistic regression, Bayesian analysis, deep learning, and ensemble methods
• Develop ML solutions for domains such as object detection, tracking, classification, terrain mapping, AR/VR, and multimodal systems
• Train, retrain, and fine-tune ML models to improve performance and accuracy
• Deploy ML models into production and conduct simulations for testing and validation
• Automate training, deployment, and retraining using CI/CD/CT and MLOps best practices
• Implement model versioning, traceability, monitoring, and lifecycle management
• Monitor production systems for latency, cost, performance, and concept drift
  

Skills Required

• Strong proficiency in Python
• Solid foundation in Machine Learning, Deep Learning, and AI algorithms
• Experience with LLMs, prompt engineering, embeddings, and retrieval-based systems
• Hands-on experience with LLMs for ML
• Strong understanding of APIs, microservices, and ML system integration
• Excellent problem-solving and analytical skills
  

Skills Preferred

• Experience with BigQuery or large-scale data platforms
• Familiarity with vector databases and embedding stores
• Exposure to model optimization techniques (quantization, distillation, ONNX, GGUF)
  

Experience Requirements

• 8+ years of overall experience
• Advanced proficiency in at least one programming language or practical experience in two or more
• Ability to guide solution design and technical direction
  

Preferred Experience

• CI/CD for ML using tools such as GitHub Actions or Jenkins
• Experience with cloud platforms (AWS, GCP, or Azure) and AI/ML services
• Strong ownership mindset: ability to take ML solutions from PoC to production
• Ability to identify the right ML approach (fine-tuning, retrieval, prompting, multimodal pipelines)
• Experience estimating feasibility, cost, timelines, and system complexity
  

• End-to-end ML system design, including:
• LLM orchestration (OCR + embeddings + prompt pipelines)
• Preprocessing for images, PDFs, PPTs, and spreadsheets
• Asynchronous processing, job orchestration, and queue-based systems
• GPU/CPU deployment strategies
• Proven experience in scaling ML systems for:
• Batch processing of large files
• High concurrency, throughput, and low latency
  

• Bachelor’s or Master’s degree in Engineering, Computer Science, Data Science, or related fields
• Relevant certifications are a plus