Role Overview :

We are looking for an experienced Senior Gen AI Engineer to design, develop, and deploy next-generation Artificial Intelligence solutions. The ideal candidate will have strong expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, Python, Data Engineering, and MLOps.

The candidate will be responsible for building scalable AI applications, productionizing ML/AI pipelines, developing intelligent automation solutions, and collaborating with engineering and business teams to deliver enterprise-grade AI capabilities.

Key Responsibilities :

1. Generative AI Solution Development :

- Design and develop enterprise-level Generative AI applications using LLM-based architectures.

- Build and optimize RAG pipelines for knowledge retrieval, question answering, and AI-powered solutions.

- Develop AI Agent workflows using agent orchestration frameworks.

- Integrate LLMs with enterprise systems, APIs, and data platforms.

- Implement prompt engineering techniques to improve AI response quality and accuracy.

2. AI Application Engineering :

- Develop scalable backend services and AI applications using Python.

- Build APIs and services to support AI-powered applications.

- Design solutions for LLM integration, data processing, and AI automation.

- Work on AI application architecture, performance optimization, and scalability.

3. Data Engineering & AI Pipelines :

- Design and develop data pipelines required for AI/ML workloads.

- Perform data extraction, transformation, validation, and preparation.

- Develop optimized SQL queries and work with structured and unstructured data.

- Implement data workflows supporting RAG and AI applications.

4. MLOps & Production Deployment :

- Deploy and maintain AI/ML solutions in production environments.

- Build CI/CD pipelines for AI application deployment.

- Implement model monitoring, versioning, and performance tracking.

- Work with containerization and orchestration technologies.

5. AI Research & Innovation :

- Evaluate emerging GenAI technologies and frameworks.

- Research improvements in LLM performance, RAG architecture, and AI Agent capabilities.

- Recommend AI-driven solutions for business problems.

- Contribute to AI best practices and knowledge-sharing initiatives.

Required Skills :

Mandatory Technical Skills :

- 8 - 12 years of software engineering experience with strong AI/ML exposure.

- Advanced programming experience in Python.

- Hands-on experience with Generative AI and Large Language Models (LLMs).

- Strong experience building RAG (Retrieval-Augmented Generation) solutions.

- Experience developing AI Agents / Agentic AI workflows.

- Strong understanding of :

1. Prompt Engineering

2. Embeddings

3. Vector Search

4. Model Evaluation

5. Responsible AI practices.

- Strong SQL and Data Engineering knowledge.

- Experience building and deploying production ML/AI pipelines.

- Hands-on experience with cloud platforms : 1. AWS 2. Azure 3. GCP.

2. Required Frameworks & Tools :

- GenAI Frameworks :

1. LangChain

2. LangGraph

3. LlamaIndex.

- AI/ML Frameworks :

1. PyTorch

2. TensorFlow

3. Scikit-learn

4. Hugging Face Transformers.

- RAG & Vector Databases :

1. FAISS

2. Pinecone

3. ChromaDB

4. Weaviate

5. Milvus.

- MLOps & Deployment :

1. MLflow

2. Docker

3. Kubernetes

4. CI/CD tools (Jenkins, GitHub Actions, Azure DevOps).

Good-to-Have Skills :

- Experience with Azure OpenAI / OpenAI APIs.

- Knowledge of Snowflake, Databricks, or data warehouse platforms.

- Experience with Apache Spark and Airflow.

- Knowledge of REST APIs and microservices architecture.

- Experience designing scalable enterprise AI platforms.

- Exposure to AI governance and security practices.