AI Engineer

Role Overview

We are seeking a highly skilled AI Engineer to design, develop, deploy, and optimize Artificial Intelligence and Machine Learning solutions that address complex business challenges. The ideal candidate will have expertise in machine learning, deep learning, Generative AI, Large Language Models (LLMs), data engineering, and cloud platforms, with the ability to build scalable AI-powered applications for enterprise environments.

Key Responsibilities

AI & Machine Learning Development

• Design, develop, and deploy machine learning and deep learning models.
• Build AI-powered solutions for prediction, recommendation, classification, and automation.
• Develop and optimize Generative AI applications using Large Language Models (LLMs).
• Fine-tune foundation models for domain-specific use cases.

Generative AI & LLM Engineering

• Develop applications using OpenAI, Azure OpenAI, Gemini, Claude, Llama, or similar models.
• Implement Retrieval-Augmented Generation (RAG) architectures.
• Design prompt engineering strategies and AI orchestration workflows.
• Build AI agents and conversational AI solutions.
• Evaluate model performance, bias, accuracy, and reliability.

Data Engineering & Analytics

• Collect, clean, process, and analyze large datasets.
• Design and implement data pipelines for AI workloads.
• Work with structured and unstructured data sources.
• Develop feature engineering and model training frameworks.

MLOps & Deployment

• Deploy AI models into production environments.
• Implement CI/CD pipelines for AI applications.
• Monitor model performance and drift.
• Establish model governance, versioning, and observability frameworks.
• Automate retraining and deployment processes.

Cloud & Platform Engineering

• Build AI solutions on AWS, Azure, or Google Cloud Platform.
• Utilize AI/ML services such as Azure AI Services, SageMaker, Vertex AI, or equivalent platforms.
• Optimize model performance, scalability, and cost efficiency.
• Ensure secure deployment of AI applications.

Required Skills

Programming Languages

• Python (Mandatory)
• SQL
• Knowledge of Java, Go, or JavaScript is an advantage.

AI & Machine Learning

• Strong understanding of Machine Learning algorithms and techniques.
• Deep Learning using TensorFlow, PyTorch, or Keras.
• Natural Language Processing (NLP).
• Computer Vision concepts.
• Model evaluation and optimization.

Generative AI Technologies

• Large Language Models (LLMs).
• Prompt Engineering.
• RAG (Retrieval-Augmented Generation).
• Vector Databases such as Pinecone, ChromaDB, Weaviate, FAISS, or Milvus.
• AI Agent Frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel.

Data & Database Technologies

• SQL and NoSQL databases.
• Data Warehousing concepts.
• Data preprocessing and feature engineering.
• Distributed data processing platforms.

MLOps & DevOps

• MLflow, Kubeflow, Airflow, or equivalent tools.
• Docker and Kubernetes.
• CI/CD pipelines.
• Git and version control systems.

Cloud Platforms

• Microsoft Azure
• Amazon Web Services (AWS)
• Google Cloud Platform (GCP)

Preferred Skills

• Experience with enterprise AI implementations.
• Knowledge of Responsible AI and AI Governance.
• Experience in Retail, E-Commerce, Banking, Healthcare, or Supply Chain domains.
• Familiarity with Reinforcement Learning and Multi-Agent Systems.
• Exposure to Knowledge Graphs and Semantic Search.
• Understanding of AI security and compliance requirements.

Educational Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.

Soft Skills

• Strong analytical and problem-solving abilities.
• Excellent communication and stakeholder management skills.
• Ability to translate business requirements into AI solutions.
• Strong collaboration and teamwork skills.
• Innovation mindset with a passion for emerging AI technologies.

Key Responsibilities / Success Metrics

• Successful deployment of AI solutions into production.
• Model accuracy and performance improvement.
• Reduction in manual effort through automation.
• Delivery of scalable and cost-efficient AI platforms.
• Business value generated through AI initiatives.
• Compliance with Responsible AI and governance standards.

Nice to Have Certifications

• Microsoft Azure AI Engineer Associate
• AWS Certified Machine Learning Specialty
• Google Professional Machine Learning Engineer
• TensorFlow Developer Certification
• Databricks Machine Learning Certification