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
