AI Engineer

Tech Stack: Python, TensorFlow / PyTorch, Machine Learning, Deep Learning, NLP, LLMs, Data Pipelines, APIs, Azure / AWS

Who We Are Looking For

We are looking for an AI Engineer with strong expertise in designing, developing, and deploying machine learning and AI-driven solutions. You will lead the development of intelligent systems—ranging from predictive models to LLM-based applications—while also contributing to data pipeline engineering, model optimization, and API integrations.

This role requires someone who can work across the full AI lifecycle: data preprocessing, model experimentation, model training, evaluation, deployment, and monitoring. Strong experience with Python, machine learning frameworks, and cloud-based AI services is essential.

You will also support AI integration work across cloud platforms (Azure, AWS, or GCP), particularly around deploying models, managing inference endpoints, and enabling secure access to AI/ML resources. This requires hands-on experience with cloud services related to AI workloads, identity management, and compute resources.

What Skills and Experience You Will Bring

Strong proficiency in:

• Python (NumPy, Pandas, Scikit-learn)
• TensorFlow or PyTorch
• Machine Learning (supervised/unsupervised methods)
• Deep Learning architectures (CNN, RNN, Transformers)
• Natural Language Processing (NLP) and LLMs
• API development & integration (REST, FastAPI, Flask)
• Data pipelines (ETL, feature engineering, preprocessing)
• Experience building and deploying end-to-end ML solutions.
• Ability to write clean, maintainable, modular, and well-documented code.
• AI / Model Development Expertise
• Demonstrated experience training, evaluating, and optimizing ML and deep learning models.
• Understanding of vectorization, embeddings, and model performance tuning.

Practical experience with:

• Large language models (GPT, Llama, Claude, or similar)
• Model fine-tuning / prompt engineering
• Retrieval-augmented generation (RAG) frameworks
• Experience implementing inference endpoints and integrating AI into applications.
• Cloud Skills (Hands-On Experience Required)
• Hands-on experience with cloud AI services, specifically:
• Azure Machine Learning, AWS Sagemaker, or Google Vertex AI
• Model deployment, versioning, and monitoring
• Container-based deployment using Docker / Kubernetes

Understanding of:

• Secure authentication, tokens, and access control for AI workloads
• Scalable compute environments (GPU, TPU, or managed training services)
• Additional Expectations
• Strong foundation in algorithms, data structures, and mathematical modeling.
• Experience with Git, branching strategies, and CI/CD workflows.
• Strong problem-solving, debugging, and analytical skills.
• Ability to work independently in a remote or distributed team environment.
• Excellent communication skills for explaining complex concepts and delivering clear documentation.