Ai Engineer Jobs

About The Position

Requirements

• 6–8 years of hands-on experience in Software Engineering, AI Engineering, Data Engineering, or related technical disciplines.
• Demonstrated success delivering production-grade AI, Machine Learning, or Generative AI solutions.
• Experience translating business use cases into scalable AI-enabled applications.
• Strong hands-on programming expertise in Python.
• Experience developing and deploying Generative AI and LLM-powered applications.
• Strong experience with Amazon Bedrock and AWS-based AI services.
• Experience designing and implementing AI agents and agentic workflows.
• Strong understanding of agent orchestration, multi-agent architectures, tool/function calling, agent memory, context management, and prompt engineering.
• Hands-on experience with RAG, embeddings, vector databases and vector search, knowledge retrieval systems, and enterprise document processing.
• Strong Snowflake, SQL, and data integration experience.
• Experience integrating AI solutions with REST APIs, enterprise applications, databases, and internal and external services.
• Experience deploying, supporting, and monitoring AI solutions in production environments.
• Strong understanding of authentication and authorization, enterprise security and data privacy, secrets management, responsible AI practices, and AI governance and guardrails.
• Excellent analytical, problem-solving, and debugging skills.
• Strong verbal and written communication skills.
• Ability to collaborate effectively with engineering and business stakeholders.
• Demonstrated ability to work in fast-paced, highly collaborative environments.

Nice To Haves

• Experience with Agentic AI frameworks such as CrewAI, LangGraph, AutoGen, or similar orchestration frameworks.
• Experience with Model Context Protocol (MCP).
• Experience building complex multi-agent collaboration systems.
• Experience with AI evaluation, monitoring, and observability frameworks.
• Experience using Kiro or other AI-assisted development tools.
• Experience with OpenShift (OCP), containers, and cloud-native deployments.
• Experience implementing CI/CD pipelines and DevOps practices for AI applications.
• Familiarity with Snowflake Cortex AI capabilities.
• Knowledge of enterprise AI governance, model monitoring, and responsible AI principles.

Responsibilities

• Design, develop, test, and deploy Generative AI and Agentic AI applications using Amazon Bedrock, AWS, and related AI technologies.
• Build intelligent AI agents and multi-agent systems to automate and enhance business workflows.
• Develop scalable AI-powered solutions that integrate with enterprise applications and platforms.
• Design and implement agent orchestration patterns, including supervisor and coordinator agents, specialized task agents, routing and decisioning frameworks, context and memory management, and workflow execution and monitoring.
• Build multi-step reasoning workflows that combine LLM capabilities with enterprise systems and business processes.
• Integrate AI agents with REST APIs, enterprise applications, internal platforms and services, databases and data platforms, and external tools and third-party systems.
• Create secure, reusable tools, APIs, and integration layers for agent interaction with enterprise systems.
• Design and deliver RAG solutions using enterprise documents, structured data, and knowledge repositories.
• Implement embeddings, vector search, document retrieval, and knowledge-grounding strategies; select foundation models based on business and technical requirements.
• Develop backend AI services primarily using Python.
• Design prompts, agent instructions, tool definitions, workflows, and AI guardrails.
• Deploy and support AI applications in AWS and OpenShift (OCP) environments.
• Implement logging, monitoring, evaluation, security, observability, and operational controls for enterprise AI workloads.
• Collaborate with engineering, architecture, product, and business teams to identify and deliver AI-enabled solutions.
• Troubleshoot and resolve issues involving models, agents, APIs, integrations, and downstream systems.
• Establish reusable engineering patterns, best practices, and governance standards for Agentic AI development.
• Provide technical leadership and mentorship to engineers and development teams.

Benefits

• Competitive compensation, including base pay and annual incentive.
• Comprehensive health and life insurance and well-being benefits, based on location.
• Pension and retirement benefits.
• Paid time off and personal/family care, and other leaves of absence to support physical, financial, and emotional well-being.
• Flexible/hybrid work model with three days onsite and two days remote.
• Investment in professional development.