AI Engineer

Key Responsibilities

AI & Machine Learning Development

- Design, develop, and deploy AI/ML models for business use cases.
- Build and optimize machine learning pipelines from data preparation to model deployment.
- Develop predictive, classification, recommendation, and NLP solutions.
- Fine-tune and evaluate foundation models and LLMs.

Generative AI Solutions

- Design and implement Generative AI applications using LLMs.
- Develop Retrieval-Augmented Generation (RAG) solutions.
- Create prompt engineering frameworks and reusable AI workflows.
- Integrate AI copilots, chatbots, and virtual assistants into enterprise platforms.
- Implement AI governance, monitoring, and responsible AI practices.

Data Engineering & Integration

- Collaborate with Data Engineers and Data Scientists to prepare training datasets.
- Develop APIs and microservices for AI model consumption.
- Integrate AI services with enterprise systems and cloud platforms.

Model Deployment & Monitoring

- Deploy AI solutions using MLOps best practices.
- Monitor model performance, accuracy, drift, and reliability.
- Ensure scalability, security, and compliance requirements are met.

Required Technical Skills

AI / ML Technologies

- Machine Learning Algorithms
- Deep Learning
- Natural Language Processing (NLP)
- Generative AI
- Large Language Models (LLMs)
- Prompt Engineering
- Vector Databases

Programming Languages

- Python (Mandatory)
- SQL
- Optional: Java, JavaScript, or Scala

AI Frameworks & Libraries

- TensorFlow
- PyTorch
- Scikit-learn
- LangChain
- LlamaIndex
- Hugging Face Transformers
- OpenAI APIs / Azure OpenAI

Vector Databases

- Pinecone
- Weaviate
- ChromaDB
- FAISS

Cloud & MLOps

- Azure AI Services / Azure OpenAI
- AWS Bedrock / SageMaker
- Google Vertex AI
- MLflow
- Docker
- Kubernetes
- CI/CD Pipelines

Database Technologies

- SQL Server
- PostgreSQL
- MongoDB
- Cosmos DB

Leadership & Soft Skills

- Strong analytical and problem-solving skills.
- Excellent communication and stakeholder management.
- Ability to translate business problems into AI-driven solutions.
- Experience working in Agile/Scrum environments.
- Strong collaboration with cross-functional teams.

Preferred Qualifications

- Experience building enterprise AI assistants and chatbots.
- Hands-on experience with RAG architectures and vector search.
- Knowledge of Responsible AI, AI Safety, and AI Governance.
- Exposure to Agentic AI frameworks and autonomous AI workflows.
- Experience with Retail, E-commerce, BFSI, Healthcare, or Supply Chain domains.

Educational Qualification

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

Preferred Certifications

- Microsoft Azure AI Engineer Associate (AI-102)
- Microsoft Azure Data Scientist Associate (DP-100)
- AWS Machine Learning Specialty
- Google Professional Machine Learning Engineer
- TensorFlow Developer Certificate

Key Competencies

- Artificial Intelligence
- Generative AI
- Machine Learning
- Prompt Engineering
- LLM Development
- NLP
- RAG Architecture
- MLOps
- Data Analytics
- Innovation & Problem Solving