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Job Description

Role Overview :

We are looking for an AI Engineer to build production-grade AI solutions using NLP, LLMs, RAG, Deep Learning, and Generative AI for financial services use cases.

Total Experience : 3 - 5 years

Key Responsibilities :

- Build AI applications using LLMs, NLP, and Deep Learning for Q&A, summarization, document intelligence, and semantic search.

- Design and optimize RAG pipelines using structured/unstructured data and vector databases.

- Integrate and orchestrate LLMs/SLMs using LangChain, LangGraph, AI Agents, and advanced prompting techniques.

- Develop scalable REST APIs using Python, FastAPI, or Flask.

- Implement semantic/hybrid search using Elasticsearch, FAISS, Pinecone, Weaviate, or similar technologies.

- Develop NLP models for NER, classification, intent detection, sentiment analysis, embeddings, and summarization.

- Fine-tune and evaluate AI models for accuracy, relevance, and latency.

- Apply LLMOps/MLOps practices for monitoring, evaluation, versioning, and production support.

- Deploy applications using Docker, Git, and CI/CD.

- Troubleshoot production issues, perform RCA, and implement sustainable solutions.

- Work independently with cross-functional teams to identify and deliver AI-driven improvements.

Required Skills :

- 3+ years of experience in AI/ML, NLP, Data Science, Deep Learning, or GenAI.

- Strong Python and backend/API development experience.

- Hands-on experience with LLMs, RAG, Prompt Engineering, embeddings, and AI applications.

- Experience with LangChain/LangGraph/AI Agents.

- Strong knowledge of PyTorch, TensorFlow, or Keras.

- Experience with FastAPI/Flask and high-volume APIs.

- Hands-on experience with vector databases and Elasticsearch.

- Experience with Docker, Git, CI/CD, and production deployments.

- Exposure to LLMOps/MLOps and model monitoring.

- Strong troubleshooting and RCA skills.

Good to Have :

- Azure OpenAI or AKS experience.

- Knowledge of PostgreSQL, MongoDB, Redis, or Kafka.

- Experience in FinTech, Banking, SaaS, or AI-first product companies.

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