About the Role :

We are seeking a Senior Generative AI Engineer with deep expertise in Artificial Intelligence, Python development, and cloud-native architectures. This role demands a hands-on professional who can architect, build, and deploy intelligent systems leveraging Generative AI, LLMs, and cloud platforms (AWS, Azure, or GCP). You will be responsible for designing end-to-end AI solutions, integrating with enterprise data ecosystems, and ensuring high performance, scalability, and reliability across AI-driven applications.

Key Responsibilities :

AI Model Development & Integration :

- Design, develop, and fine-tune large language model (LLM)-based solutions using open-source frameworks and APIs.

- Implement Retrieval-Augmented Generation (RAG) pipelines and vector-based search using embedding techniques.

- Work with model inference, prompt engineering, and model optimization for production-grade deployment.

Backend Development :

- Develop robust and scalable RESTful APIs using Python (FastAPI) and integrate AI capabilities with business applications.

- Architect microservices-based systems ensuring high availability, modularity, and maintainability.

Data Infrastructure & Cloud Deployment :

- Design and implement data ingestion pipelines using Kafka, Azure Event Hub, or AWS Kinesis.

- Work with structured and unstructured data leveraging SQL and NoSQL databases (PostgreSQL, MongoDB, DynamoDB, etc.).

- Deploy and manage AI workloads on AWS, Azure, or Google Cloud Platform (GCP) using managed services.

MLOps & CI/CD :

- Develop and manage CI/CD pipelines using Git, Jenkins, and Azure DevOps for model training, testing, and deployment.

- Ensure automated testing, monitoring, and rollback mechanisms for reliable AI systems.

Collaboration & Delivery :

- Work closely with cross-functional teams (data engineers, product managers, and business analysts) to translate requirements into technical deliverables.

- Participate in Agile/Scrum development cycles, ensuring timely and high-quality deliveries.

Technical Skills & Competencies :

Core Technical Expertise :

- Hands-on experience with Generative AI, LLMs, and open-source AI frameworks (LangChain, Hugging Face, OpenAI API, etc.)

- Strong programming skills in Python with FastAPI for API and backend development.

- Proficiency in JavaScript (Node.js/React) for full-stack integration (good to have).

- Deep understanding of microservices architecture and containerized deployments (Docker, Kubernetes).

Cloud & Infrastructure :

- Experience across AWS, Azure, or GCP with proficiency in AI and data services.

- Practical experience deploying AI solutions in cloud-native environments.

Data Systems :

- Strong command of SQL and NoSQL databases for performance tuning and data modeling.

- Experience with streaming data platforms like Kafka, Azure Event Hub, or AWS Kinesis.

DevOps & Automation :

- Experience in CI/CD, Git-based version control, and infrastructure-as-code (Terraform, CloudFormation preferred).

- Familiarity with monitoring tools and AI observability best practices.

Desired Skills :

- Expertise in AWS (4/5 proficiency level) and SQL (4/5 proficiency level).

- Practical understanding of data engineering pipelines, ETL, and data governance.

- Knowledge of machine learning lifecycle management and model explainability frameworks.

- Exposure to Vector Databases such as Pinecone, FAISS, or Chroma.

Qualifications :

- Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Engineering, or related discipline.

- 8+ years of total experience with at least 3 years in AI/ML or Generative AI development.

- Proven record of delivering AI-powered applications in production environments.