ML Infrastructure Engineer

Who are we looking for? We are looking for over 15+ years of experience and 7+ years of experience AI Engineer, who would have experience in artificial intelligence engineering to join the revolution, using RAG frameworks, GenAI tools, AWS infra background, LangChain, LLMs, and LangGraph to help various business improvements.

Technical Skills :

Four or more years of experience with AWS Infra background on AI, General AI tools, AWS Bedrock, Python, integration with Claude LLM, Palantir

Strong hands-on experience in RAG frameworks, LangChain, LLMs, AWS, GenAI, and LangGraph

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

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

Familiarity with the AWS ecosystem, specifically Redshift and RDS

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

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

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

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

Should have experience with cloud platforms like AWS, Azure, and Google Cloud Platform

Experience in insurance domain is an added advantage

Professional certification.

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 back-end systems

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