AI Engineer

Job Summary

We are seeking an innovative and highly skilled AIEngineer to design, develop, deploy, and optimize Artificial Intelligence and Generative AI solutions. The ideal candidate will have expertise in Machine Learning, Large Language Models (LLMs), Natural Language Processing (NLP), AI application development, and cloud-based AI platforms. The role involves building intelligent systems that drive automation, enhance decision-making, and deliver business value.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 4+ years of experience in AI/ML development.
• Hands-on experience building and deploying machine learning models.
• Strong proficiency in Python and AI/ML frameworks.
• Experience with Generative AI, LLMs, and RAG architectures.
• Experience working with cloud AI services and APIs.
• Understanding of responsible AI and model governance.
• Strong analytical and problem-solving abilities.

Preferred Qualifications

• Experience with Agentic AI and Autonomous AI Systems.
• Azure AI Engineer Associate, AWS Machine Learning, or GCP AI certifications.
• Knowledge of MLOps and model deployment best practices.
• Experience in Retail, E-Commerce, Banking, Healthcare, or Supply Chain domains.
• Familiarity with AI observability and monitoring tools.

Key Competencies

• Artificial Intelligence Development
• Generative AI Solution Design
• Problem Solving
• Analytical Thinking
• Innovation and Research
• Stakeholder Communication
• Collaboration and Teamwork
• Continuous Learning

Key Performance Indicators (KPIs)

• Model Accuracy and Performance
• AI Solution Adoption Rate
• Response Quality of AI Systems
• Time-to-Production for AI Solutions
• Cost Optimization of AI Workloads
• Model Reliability and Availability
• Business Impact Delivered

Key Responsibilities

• Design, develop, and deploy AI/ML and Generative AI solutions.
• Build and optimize machine learning models for business use cases.
• Develop AI-powered applications using LLMs and NLP techniques.
• Implement Retrieval-Augmented Generation (RAG) frameworks and AI agents.
• Fine-tune and evaluate foundation models for specific business requirements.
• Integrate AI services with enterprise applications and APIs.
• Develop data pipelines for model training, validation, and deployment.
• Monitor model performance, accuracy, and reliability in production environments.
• Collaborate with Data Scientists, Architects, Product Owners, and Engineering teams.
• Ensure AI solutions comply with security, privacy, and responsible AI standards.
• Stay updated with advancements in AI, GenAI, Agentic AI, and MLOps.

Required Technical Skills

Artificial Intelligence & Machine Learning

• Machine Learning Algorithms
• Deep Learning
• Supervised & Unsupervised Learning
• Model Training and Optimization
• Feature Engineering
• Model Evaluation and Validation

Generative AI

• Large Language Models (LLMs)
• OpenAI, Azure OpenAI
• Gemini, Claude, Llama
• Prompt Engineering
• Fine-Tuning Techniques
• RAG (Retrieval-Augmented Generation)
• Vector Databases

Agentic AI Frameworks

• LangChain
• LangGraph
• CrewAI
• Semantic Kernel
• AI Agents & Multi-Agent Systems

Programming Languages

• Python (Mandatory)
• SQL
• JavaScript (Good to Have)

AI/ML Frameworks

• TensorFlow
• PyTorch
• Scikit-learn
• Hugging Face Transformers

Databases

• PostgreSQL
• SQL Server
• MongoDB

Vector Databases

• Pinecone
• Weaviate
• ChromaDB
• FAISS
• Azure AI Search

Cloud Platforms

• Microsoft Azure
• AWS
• Google Cloud Platform (GCP)

MLOps & DevOps

• MLflow
• Kubeflow
• Docker
• Kubernetes
• GitHub Actions
• Azure DevOps
• Jenkins

Data Engineering

• Pandas
• NumPy
• Apache Spark
• Data Pipelines