At a glanceSummarised by Seekless from the posting.
Must have19
Develop Algorithms that enable AI agents to perform tasks without step-by-step instructions
Design and implement agentic AI systems capable of autonomous decision-making
Design and develop multi-agent frameworks using tools such as LangGraph, Crew AI, or Semantic Kernel
Design and develop machine learning techniques that allow agents to learn from experience
Translate business requirements into agentic AI solutions
Collaborate with data scientists, software engineers, business stakeholders, and product teams
Conduct context and prompt engineering using zero-shot, few-shot, and chain-of-thought techniques
Optimize agents based on different models for performance, scalability, and accuracy
Document processes, models, and code for transparency and reproducibility
Conduct research and stay up to date with the latest advancements in AI/ML technologies
Proficiency in programming languages such as Python, Java, or C++
Strong foundation in AI/ML algorithms, data structures, and software engineering principles
Understanding of cloud platforms (e.g., AWS, Azure, GCP) for deploying AI solutions
Familiarity with MLOps tools and practices (e.g., MLflow, Kubeflow, Docker for CI/CD)
Solid knowledge of data structures, algorithms, and software engineering principles
Experience with agent orchestration, LLMOps, and model lifecycle management, vector search, information retrieval, graph algorithms and knowledge graph
Experience with version control systems (e.g., Git)
Familiarity with agent orchestration platforms and enterprise integration
Strong problem-solving skills and ability to work independently and collaboratively
Nice to have6
Experience with natural language processing (NLP), computer vision, or reinforcement learning
Knowledge of generative AI and foundation models (e.g., Gemini, Claude)
Experience with real-time inference systems and edge AI
Background in mathematics, statistics, or computational neuroscience
Understanding of LLM (Large Language Model) & LRM (Large Reasoning Model)
Understanding of AI ethics, bias mitigation, and explainable AI
Eligibility1
Ensure ethical AI practices and compliance with data privacy regulations
Skills
Python
Java
C++
AWS
Azure
GCP
MLflow
Kubeflow
Docker
Git
LangGraph
Crew AI
Semantic Kernel
Gemini
Claude
IND Senior Staff Software Engineer - GCC020
About the company
We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.
Key Responsibilities
•
Develop Algorithms that enable AI agents to perform tasks without step-by-step instructions.
•
Design and implement agentic AI systems capable of autonomous decision-making, learning from experience, and adapting to dynamic goals and contexts
•
Design and develop multi-agent frameworks using tools such as LangGraph, Crew AI, or Semantic Kernel to orchestrate intelligent workflows.
•
Design and develop machine learning techniques that allow agents to learn from experience and adapt over time.
•
Translate business requirements into agentic AI solutions.
•
Collaborate with data scientists, software engineers, business stakeholders, and product teams to integrate AI solutions into production systems.
•
Conduct context and prompt engineering using zero-shot, few-shot, and chain-of-thought techniques to enhance model performance and relevance.
•
Optimize agents based on different models for performance, scalability, and accuracy.
•
Ensure ethical AI practices and compliance with data privacy regulations.
•
Document processes, models, and code for transparency and reproducibility.
•
Conduct research and stay up to date with the latest advancements in AI/ML technologies.
Required Skills & Experience
•
Proficiency in programming languages such as Python, Java, or C++.
•
Strong foundation in AI/ML algorithms, data structures, and software engineering principles.
•
Understanding of cloud platforms (e.g., AWS, Azure, GCP) for deploying AI solutions.
•
Familiarity with MLOps tools and practices (e.g., MLflow, Kubeflow, Docker for CI/CD).
•
Solid knowledge of data structures, algorithms, and software engineering principles.
•
Experience with agent orchestration, LLMOps, and model lifecycle management, vector search, information retrieval, graph algorithms and knowledge graph.
•
Experience with version control systems (e.g., Git).
•
Familiarity with agent orchestration platforms and enterprise integration.
•
Strong problem-solving skills and ability to work independently and collaboratively.
Preferred Skills & Experience
•
Experience with natural language processing (NLP), computer vision, or reinforcement learning.
•
Knowledge of generative AI and foundation models (e.g., Gemini, Claude).
•
Experience with real-time inference systems and edge AI.
•
Background in mathematics, statistics, or computational neuroscience.
•
Understanding of LLM (Large Language Model) & LRM (Large Reasoning Model)
•
Understanding of AI ethics, bias mitigation, and explainable AI.
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