AI Application Architect

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 Finetune models using techniques like LoRA and QLoRA Build AIpowered features such as: Chatbots and virtual assistants Text summarization and extraction Questionanswering systems SpeechtoText and TexttoSpeech 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 architectures 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: Fewshot prompting ChainofThought (CoT) Knowledgebase 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 Pythonbased 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 AIassisted development tools such as Microsoft Copilot to accelerate development responsibly Required Technical Skills Programming & Frameworks

Strong proficiency in Python NumPy, Pandas, Scikitlearn, TensorFlow, PyTorch, spaCy, NLTK Experience building productiongrade 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 finetuning and evaluation tools Nice to Have Skills:

Experience with enterprise AI platforms Knowledge of MLOps pipelines Understanding of AI governance, ethics, and security Prior experience in financial services or enterprise domains Soft Skills & Expectations:

Strong problemsolving and analytical thinking Ability to translate business problems into AI solutions Excellent communication with technical and nontechnical stakeholders Fast learner with a mindset to adapt to evolving AI technologies Typical Experience Range:

3-6 years for midlevel AI Engineer 7+ years for senior / lead AI Engineer roles (with handson AI/ML and GenAI experience)