Gen AI Developer

Key Responsibilities

Generative AI Application Development:

• Design, develop, and deploy GenAI-driven applications to address complex business challenges.
• Implement Retrieval-Augmented Generation (RAG) frameworks to improve information retrieval and response accuracy.

Data Management & Optimization:

• Design and optimize document chunking strategies tailored to specific datasets and use cases.
• Build, manage, and optimize data embeddings for high-performance similarity searches across vector databases.

Collaboration & Integration:

• Work closely with data engineers and scientists to integrate AI solutions into existing pipelines.
• Collaborate with cross-functional teams to ensure seamless AI implementation.

Cloud & AI Platform Utilization:

• Explore and implement best practices for utilizing cloud-native AI platforms, such as Amazon Bedrock and Azure OpenAI, to enhance solution delivery.

Continuous Learning & Innovation:

• Stay updated with the latest trends and emerging technologies in the GenAI and AI/ML fields, ensuring our solutions remain cutting-edge.

Experience:

• 3 to 5 years of overall experience in software development, with at least 3 to 4 years focused on AI/ML.
• Minimum 1 to 2 years of experience specifically working with Generative AI (GenAI) technologies.
• Python, PySpark and SQL knowledge is necessary for tasks
• Proven ability to work in a collaborative, fast-paced, and innovative environment.

Technical Skills:

Generative AI Frameworks & Technologies:

• Expertise in Generative AI frameworks, including prompt engineering, finetuning, and few-shot learning.
• Familiarity with frameworks such as T5 (Text-to-Text Transfer Transformation).
• Strong knowledge of Retrieval-Augmented Generation (RAG) for combining LLMs with external data retrieval systems.

Data Management:

• Experience in designing chunking strategies for different datasets.
• Expertise in data embedding techniques and experience with vector databases like Pinecone, Weaviate, or Milvus.

Programming & AI/ML Libraries:

• Strong programming skills in Python.
• Experience with AI/ML libraries such as TensorFlow, PyTorch, and Hugging Face Transformers.

Cloud Platforms & Integration:

• Familiarity with cloud services for AI/ML workloads (AWS, Azure).
• Experience with API integration for AI services and building scalable applications.

Certifications (Optional but Desirable):

• Certification in AI/ML (e.g., TensorFlow, AWS Certified Machine Learning –Specialty).
• Certification or coursework in Generative AI or related technologies.