Machine Learning Engineer

GenAI & LLM Prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, embeddings. - Design & implement agentic AI workflows for autonomous decision-making & multi-step reasoning.

Role: Machine Learning Engineer - Computer Vision, LLM & GenAI

Location: Remote

Duration: Long term contract

Core Responsibilities

Model Development & GenAI
- Build & deploy computer vision models for image classification, object detection, segmentation
- Develop LLM-based solutions for text analysis, content generation, information extraction
- Design & implement agentic AI workflows for autonomous decision-making & multi-step reasoning
- Prompt engineering & optimization for domain-specific LLM tasks
- Implement core ML algorithms with full understanding (not just library usage)
- Fine-tune foundation models for specialized use cases

Algorithm & Model Expertise
- Deep understanding of ML algorithms (supervised, unsupervised, reinforcement learning)
- Model evaluation, validation, and performance optimization
- Hyperparameter tuning and experimentation frameworks
- Bias detection and model fairness assessment

MLOps & Cloud-Native Development
- Deploy models to production across AWS & GCP ecosystems
- Implement model versioning, A/B testing, performance tracking
- Ensure model governance, reproducibility & compliance
- Optimize cloud infrastructure for cost efficiency

Required Skills

Technical (Must-Have)
- Python (pandas, scikit-learn, PyTorch/TensorFlow, OpenCV)
- Computer Vision Image classification, object detection, segmentation, feature extraction
- GenAI & LLM Prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, embeddings
- Agentic AI:Multi-step reasoning, tool use, agent frameworks (LangChain, AutoGen, CrewAI)
- ML algorithms from scratch (not just library usage)
- Statistical analysis & experimental design