Artificial Intelligence Engineer - PyTorch/Tensorflow

About The Role

Key Responsibilities

AI/ML Model Development :

• Build and fine-tune ML models for NLP, CV, and multimodal applications using PyTorch, TensorFlow, and Hugging Face.
• Apply optimization techniques such as quantization, pruning, distillation, and parameter-efficient fine tuning (LoRA, Adapters).
• Perform large-scale training and distributed learning where needed.
  

Generative AI & LLM Engineering

• Develop LLM-powered assistants using models like GPT, LLaMA, Falcon, etc.
• Apply prompt engineering, domain adaptation, and advanced fine-tuning techniques.
  

AI Agent & Workflow Automation

• Build AI agents with LangChain, LangGraph, or similar frameworks to automate workflows.
• Maintain reproducible and observable pipelines using MLflow, Airflow, LangSmith, etc.
  

Model Deployment & MLOps

• Deploy ML models using Docker, Kubernetes, and cloud platforms (AWS/Azure/GCP).
• Build scalable APIs using FastAPI with monitoring, logging, and load balancing.
• Implement CI/CD pipelines for automated ML deployments.
  

Monitoring, Optimization & Reliability

• Track model performance, detect drift, and implement automated retraining.
• Optimize models for production workloads and reduce latency/resource usage.
  

Collaboration

• Work closely with engineering, product, and data teams to ship AI features.
• Contribute to architectural decisions and documentation.
  

Required Qualifications

Core AI/ML Expertise :

• 3+ years of proven experience in building ML models from scratch, including training, tuning, and optimization.
• Strong hands-on experience with LLMs, fine-tuning methods (LoRA/Adapters), and GenAI workloads.
• Experience deploying ML models at scale with autoscaling, monitoring, and performance optimization.
  

Technical Skills

• Proficiency in Python and frameworks such as PyTorch, TensorFlow, Hugging Face, scikit-learn.
• Strong understanding of ML pipelines, APIs, Git, unit testing, and software engineering best practices.
• Hands-on experience with Docker, Kubernetes, and cloud ML platforms (AWS/Azure/GCP).
• Experience with MLflow, Airflow, LangSmith, Weights & Biases, or similar tools.
  

AI Agent & GenAI Development

• Experience building AI agents and workflow orchestrations using LangChain, LangGraph, etc.
• Experience building production-ready GenAI-based assistants.
  

Preferred Qualifications

• Experience with multimodal models (text, audio, video).
• Knowledge of model compression and edge deployment techniques.
• Understanding of distributed training, privacy-preserving ML, or federated learning.
• Research publications or contributions to open-source AI projects.
  

Education

• Bachelors degree in Computer Science, AI/ML, Data Science, or related field.
• Masters degree or cloud ML certifications (AWS/GCP/Azure) are a plus.
  

What We Offer

• Work on cutting-edge AI/GenAI solutions with real-world impact.
• Access to high-performance compute and modern ML tooling.
• A collaborative, innovation-led environment.
• Competitive salary, benefits, and growth opportunities.