AI Engineer

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.

Python (Mandatory)

SQL

Knowledge of Java, Go, or JavaScript is an advantage.

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.

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.

SQL and NoSQL databases.

Data Warehousing concepts.

Data preprocessing and feature engineering.

Distributed data processing platforms.

MLflow, Kubeflow, Airflow, or equivalent tools.

Docker and Kubernetes.

CI/CD pipelines.

Git and version control systems.

Microsoft Azure

Amazon Web Services (AWS)

Google Cloud Platform (GCP)

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.

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

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.

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.

Microsoft Azure AI Engineer Associate

AWS Certified Machine Learning Specialty

Google Professional Machine Learning Engineer

TensorFlow Developer Certification

Databricks Machine Learning Certification

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.

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.

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.

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.

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.