Artificial Intelligence Engineer - Data Modeling

We are looking for 7+ years experience AI Engineer, who would have an experience in
artificial intelligence engineering to join the revolution, using deep learning, neuro linguistic
programming (NLP), computer vision, chatbots and robotics to help various business
improvements.

Technical Skills :

- Familiarity with the AWS ecosystem, specifically Redshift and RDS

- Experience in RAG (Retrieval-augmented generation) modelling

- Experience with ML, deep learning, TensorFlow, Python, NLP

- Knowledge of basic algorithms, object-oriented and functional design principles, and
best-practice patterns

- Experience in REST API development, NoSQL database design, and RDBMS design and
optimization

- Communication skills, especially for explaining technical concepts to nontechnical
business leaders

- Experience in insurance domain

- Professional certification.

- Strong understanding of distributed systems : They need to understand the complexities
of modern architecture, including microservices, cloud-native environments, and hybrid
infrastructure.

- Proficiency in observability tools : They are familiar with tools for logging, metrics, and
tracing, such as ELK Stack, Prometheus, Grafana, and distributed tracing systems.

- Scripting and automation : They can automate tasks and create scripts to manage
observability infrastructure.

- Should have experience with cloud platforms like AWS, Azure, and GCP

Key Responsibilities and Skill :

- Analyze and explain AI and machine learning (ML) solutions while setting and
maintaining high ethical standards

- Advise C-suite executives and business leaders on a broad range of technology, strategy,
and policy issues associated with AI

- Work on functional design, process design (including scenario design, flow mapping),
prototyping, testing, training, and defining support procedures, in collaboration with an
advanced engineering team and executive leadership

- Understand company and client challenges and how integrating AI capabilities can help
lead to solutions

- Understand the application of architecture to instrument them for observability. Need to
automate the onboarding of applications in a factory model.

- Use agile software development processes to make iterative improvements to our backend systems

- Develop models that can be used to make predictions and answer questions for the
overall business.

- Metric & Instrumentation Standards : Defining common metric standards for every stage
of the Application Lifecycle process and Instrumentation standards and scripting including
OTel standards alignment

- Collaboration and Communication : They collaborate with development, SRE, and other
teams to ensure observability practices are integrated into workflows and to share insights.