Applied AI Engineer

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 contemporary 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

📌 Ai Ml Engineer (Bengaluru)
🏢 Sutherland
📍 Bengaluru