AI/GenAI Engineer

Responsibilities

• Lead the design and development of an Agentic AI platform
• Architect and implement core systems for agent-based AI workflows
• Apply GenAI for documentation use cases such as extraction, indexing, summarization, classification, bundling and routing.
• Design and develop RAG/Graph RAG with ingestion pipelines using langchain or AWS Bedrock.
• Develop conversation intelligence solutions using any Gen AI tech stack. Ex: LangChain,Streamlit, Copilot Studio or AWS Q Developer.
• Finetune LLM Foundational models on AWS Bedrock.
• Design and Develop LLM and AI application evaluation sets. 
• Develop and optimize ML models, pipelines, and orchestration logic
• Provide technical leadership and guidance to development teams on leveraging the self-service platform for their AI-based initiatives.
• Collaborate with stakeholders to define key performance indicators (KPIs) and establish metrics for measuring the effectiveness and adoption of the self-service platform with improved developer experience.

• Requirements
  Bachelor’s / master’s degree in computer science, Engineering, or a related field (or equivalent work experience).
• Programming Languages: Proficiency in Python is essential.
• Agentic AI : Expertise in LangChain/LangGraph, CrewAI, Semantic Kernel/Autogen and Open AI Agentic SDK
• Machine Learning Frameworks: Experience with TensorFlow, PyTorch, Scikit-learn, and AutoML; Deep expertise in machine learning, system architecture, and AI agent frameworks to build scalable, autonomous systems
• Generative AI: Hands-on experience with generative AI models, RAG architecture, and Natural Language Processing (NLP)
• Cloud Platforms: Familiarity with AWS
• Data Engineering: Proficiency in data preprocessing and feature engineering
• Version Control: Experience with GitHub for version control
• Data Science Practices: Skills in building models, testing/validation, and deployment
• Collaboration: Experience working in an Agile framework
• RAG Architecture: Experience with data ingestion, data retrieval, and data generation using optimal methods such as hybrid search
• Understanding of AWS services such as EC2, S3, VPC, IAM, Lambda, API Gateway, and others.
• Knowledge in infrastructure as code (IaC) tools such as Terraform,.
• Understanding of cloud security best practices, compliance standards, and governance frameworks.
• Experience with containerization technologies (e.g., Docker, Kubernetes) and serverless computing.
• Experience with DevOps practices, including CI/CD pipelines, configuration management, and automated testing.
• Experience in network architecture, IDAM (Identity and Access Management), Active Directory, key vaults, and other network, security-related components.
• Excellent problem-solving skills and the ability to troubleshoot complex issues in distributed systems.
  • Effective communication and collaboration skills, with the ability to effectively interact with stakeholders at all levels.