Role Overview
An AI Engineer is responsible for designing, building, deploying, and optimizing AI, Machine Learning, and Generative AI solutions that solve real business problems. This role bridges data, models, and applications, ensuring AI solutions are scalable, reliable, and production ready.
AI Engineers work closely with product owners, data engineers, software engineers, and client stakeholders to translate requirements into intelligent systems.
Key Responsibilities
1. AI & Generative AI Development
Design and build AI and Generative AI solutions using LLMs, NLP, and deep learning models
Develop applications using OpenAI APIs, Azure OpenAI, HuggingFace, LangChain, Amazon Bedrock, and similar platforms.
Implement Retrieval Augmented Generation (RAG) pipelines using vector databases such as FAISS and Pinecone
Fine tune models using techniques like LoRA and QLoRA
Build AI powered features such as:
Chatbots and virtual assistants
Text summarization and extraction
Question answering systems
Speech to Text and Text to Speech solutions
2. Machine Learning & Deep Learning
Build and deploy ML models using:
Supervised and unsupervised learning
Regression and classification algorithms
Neural networks and ensemble techniques
Develop deep learning models using TensorFlow, PyTorch, CNNs, RNNs, LSTMs, GANs, BERT and transformer
Evaluate model performance using metrics such as Perplexity, BLEU, and ROUGE
3. Prompt Engineering
Design and optimize prompts for:
Text summarization
Information extraction
Question & Answer systems
Apply advanced prompting techniques such as:
Few shot prompting
Chain of Thought (CoT)
Knowledge base grounded prompts
4. Data & Backend Integration
Work with relational and NoSQL databases:
MS SQL Server, MySQL, PostgreSQL, MongoDB, Cassandra, HBase
Build AI services and APIs using Python based frameworks
Integrate AI models with enterprise applications and workflows
Ensure data quality, security, and compliance in AI pipelines
5. Production & Cloud Readiness
Deploy AI solutions on cloud platforms (Azure / AWS preferred)
Implement scalable and secure AI architectures
Monitor, optimize, and retrain models as required
Use AI assisted development tools such as Microsoft Copilot to accelerate
Required Technical Skills
Programming & Frameworks
Strong proficiency in Python
NumPy, Pandas, Scikit learn, TensorFlow, PyTorch, spaCy, NLTK
Experience building production grade AI pipelines
AI / ML / GenAI
LLMs and Generative AI
NLP techniques
RAG architectures
Embeddings (Word2Vec, GloVe, ELMo)
Vector databases
Cloud & Tools
Azure OpenAI / AWS Bedrock
HuggingFace ecosystem
LangChain
Model fine tuning and evaluation tools