Key Responsibilities :

AI/ML Solution Development :

- Design, build, train, and deploy scalable AI/ML models for business applications.

- Develop and implement Generative AI solutions using state-of-the-art LLMs and NLP techniques.

- Build intelligent applications leveraging LangChain and related AI orchestration frameworks.

- Evaluate model performance and continuously improve accuracy, scalability, and reliability.

Generative AI & NLP :

- Develop conversational AI, question-answering systems, summarization engines, and intelligent document processing solutions.

- Implement Retrieval-Augmented Generation (RAG) architectures for enterprise use cases.

- Work with embeddings, vector databases, prompt engineering, and LLM optimization techniques.

- Build NLP pipelines for text classification, sentiment analysis, entity recognition, and language understanding.

Software Engineering & Application Integration :

- Integrate AI/ML solutions into existing enterprise applications through APIs and microservices.

- Develop both batch and real-time inference solutions.

- Collaborate with application development teams to embed AI capabilities into business workflows.

- Ensure scalability, performance, and maintainability of AI applications.

Data Engineering & Analytics :

- Write, optimize, and maintain complex SQL queries.

- Build and optimize large-scale data pipelines using :

1. Hive

2. PySpark

3. Spark DataFrames

- Work with structured and unstructured datasets.

- Support feature engineering and data preparation activities.

Documentation & Governance :

- Create and maintain comprehensive technical documentation including :

1. Model Documentation

2. Data Dictionaries

3. Design Documents

4. Code Documentation

5. Deployment Guides

- Support model validation, explainability, auditability, and compliance requirements.

- Participate in AI/ML governance processes, particularly within regulated environments.

DevOps & MLOps :

- Utilize GitHub for version control and collaborative development.

- Implement CI/CD pipelines for automated testing, deployment, and monitoring of AI models.

- Support model lifecycle management, monitoring, retraining, and performance tracking.

- Work closely with DevOps teams to automate deployment processes.

Required Technical Skills :

AI/ML Technologies :

- Machine Learning

- Deep Learning

- Generative AI

- NLP

- LangChain

- Large Language Models (LLMs)

- Prompt Engineering

- RAG Frameworks

Programming :

- Python (Mandatory)

- Java (Preferred)

Data Technologies :

- SQL

- Hive

- PySpark

- Spark DataFrames

- NoSQL Databases (Preferred)

DevOps & Tools :

- GitHub

- CI/CD Pipelines

- Model Deployment Frameworks

- API Development

Soft Skills :

- Strong analytical and problem-solving skills

- Excellent communication and documentation abilities

- Ability to work independently and in cross-functional teams

- Stakeholder collaboration and requirement understanding

Preferred Qualifications :

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

- Experience working in highly regulated industries such as Banking, Financial Services, Healthcare, Insurance, or Telecommunications.

- Exposure to AI Governance, Responsible AI, and Model Risk Management frameworks is highly desirable.