AI / ML Architect

AI / ML Architect
Experience: 8-14+ Years
Location:  Bengluru (Remote)
Notice Period : Immediate Joiner

Role Overview
We are looking for an experienced AI / ML Architect/Lead to design, develop, and deploy enterprise-scale AI/ML and Generative AI solutions. The ideal candidate will have strong expertise in Machine Learning, Data Engineering, MLOps, LLMs, RAG architectures, and Cloud-based AI platforms, with the ability to lead end-to-end AI initiatives.

Key Responsibilities
• Design and architect scalable AI/ML and Generative AI solutions.
• Develop data pipelines, ETL/ELT processes, and feature engineering frameworks.
• Perform data preprocessing, EDA, model training, optimization, and deployment.
• Implement MLOps practices including model versioning, experimentation,monitoring, and CI/CD.
• Build and deploy RAG-based applications, AI agents, and LLM-powered solutions.
• Collaborate with business and technical stakeholders to translate requirements into AI solutions.
• Ensure AI security, governance, compliance, and best practices.
• Mentor and guide ML Engineers, Data Scientists, and AI teams.

Qualifications
• Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field.
• 8-14+ years of experience in AI/ML architecture and solution delivery.
• Proven experience building enterprise-scale AI, ML, and Generative AI applications.
• Strong leadership and stakeholder management skills.

Requirements

Required Skills

AI/ML & GenAI
• Machine Learning Algorithms
• Feature Engineering, Data Splitting, Encoding
• Model Design, Optimization & Deployment
• LLMs: OpenAI, LLaMA, Gemini, BERT
• RAG Pipelines, Prompt Engineering
• AI Agent Frameworks & Agent Deployment

Programming & Frameworks
• Python, R, Scala, Java
• Pandas, TensorFlow, PyTorch, Scikit-learn, Keras

Data & MLOps
• Data Engineering, ETL/ELT
• Git, DVC, MLflow
• Docker, Kubernetes, Kubeflow
• Apache Airflow, CI/CD

Cloud & Platforms
• AWS, Azure, GCP
• AWS SageMaker, AWS Bedrock
• Azure Machine Learning Services
• Google AI Platform
• Databricks, Snowflake, Salesforce, SAP

Databases
• RDBMS & NoSQL Databases
• Vector Databases (Pinecone, Weaviate, Chroma, FAISS, Milvus, etc.)