Description :

FRS and Call Risk :

- Neural network fundamentals : Understanding of architectures commonly used in GenAI (e.g., Transformers, GANs, diffusion models).

- Prompt engineering : Skill in designing and iterating prompts to get desired outputs from LLMs. Experience with techniques like few-shot prompting, chain-of-thought prompting, and prompt templates.

- Data preprocessing : Cleaning, transforming, and augmenting datasets to make them ML-ready. Experience with AWS GIS (Glue Interactive) and AWS Sagemaker

- Infrastructure & deployment : Familiarity with containerization (ECS/ACS), orchestration, and cloud platforms (AWS, Azure) for large-scale training and inference.

- Model monitoring & maintenance : Techniques for continuously monitoring generative output quality, detecting drift or bias, and updating models post-deployment.

- API design & integration : Knowledge of REST or GraphQL to connect GenAI services with front-end or third-party applications.

- Human feedback loops : Methods for collecting user feedback (rankings, flags, ratings) and incorporating it into iterative training or reinforcement learning from human feedback (RLHF).

- Error analysis : Ability to perform systematic analyses of generative outputs, identifying common failure modes like hallucinations, bias, or factual errors.

- User experience (UX) considerations : Understanding how to design user-friendly interfaces for interactive AI applications (e.g., chatbots, image generation tools).

- Since it may be difficult to fin one person with all above skills, we are looking for a team setting that can cover the above skills. We currently have such model with our existing vendors where we have a GenAI Development Team

CXD :

- 3+ years of experience as an AWS AI/ML development

- Python and SQL Knowledge is must.

- Strong project Hands-on experience in Spark, PySpark, Unix shell/Perl scripting and SQL.

- Strong project Hands-on experience in developing ASW Services like AWS Sagemaker, Cloudformation, IAM, Secrets, Cloudwatch, S3 & Step Functions.

- Strong project hands-on experinece on AWS Cloud formation template and deployment.

- Experience with AI and Machine Learning algorithms.

- Familiarity with AI platforms and understanding of their capabilities and limitations.

- Experience with Natural Language Processing (NLP).

- Experience with AI/ML frameworks like TensorFlow, PyTorch, etc.

- Strong Project hands-on experience in AWS Services and CI/CD Pipeline development is must.

- Knowledge about Bitbucket and Bamboo deployment is good to have.An in-depth understanding of large-scale data sets, including both structured and unstructured data.

- A critical thinker with strong research, analytics, and problem-solving skills

- Self-motivated with a positive attitude and an ability to work independently and or in a team

- Able to work under tight timeline and deliver on complex problems.