Ai Ml Engineer

Applied AI Engineer Job Description

Role Summary

The Applied AI Engineer develops, deploys, and operationalizes AI/ML solutions that solve real-world business problems at scale. This role is highly hands-on, and the ideal candidate can translate ambiguous business needs into robust AI-driven systems. From initial data exploration to model development, to deployment, monitoring, and continuous improvement.

Key Responsibilities

AI/ML Solution Development

• Build, fine-tune, and deploy models using classical ML, deep learning, NLP, computer vision and LLM-based architectures.
• Apply retrieval-augmented generation (RAG), agent frameworks, prompt engineering, and domain adaptation for production applications.
• Implement evaluation pipelines, experiment tracking, and model lifecycle management.

Software Engineering & Deployment

• Design and build scalable inference services, APIs, and microservices around AI models.
• Integrate models into cloud environments (Azure, GCP) using modern MLOps tooling.
• Optimize models for latency, throughput, resource efficiency, and cost.

Data Engineering & Feature Pipelines

• Work with structured, semi-structured, and unstructured data.
• Build data preprocessing, feature engineering, and real-time/streaming pipelines.
• Implement vector stores, embeddings pipelines, and semantic search systems.

Required Qualifications

• Bachelor’s or master’s in computer science, Engineering, Machine Learning, or related field (or equivalent practical experience).
• Strong proficiency in Python and ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face).
• Familiarity with agent frameworks (LangChain, OpenAI’s Agents API).
• Experience fine-tuning or deploying LLMs (GPT, LLaMA) or building RAG-based AI systems.
• Solid understanding of distributed systems, APIs, cloud compute, containers, and CI/CD pipelines.
• Experience with vector databases (FAISS, Pinecone, Cosmos).
• Experience using modern MLOps frameworks (MLflow, Airflow).
• Classical ML understanding (regression, classification, time series, clustering)
• Deep learning basics (NNs, CNNs, RNNs, transformers)
• Understanding of overfitting, bias/variance, model tuning