Senior AI Engineer (Python)

Jobgether; United States; Remote; USD 140,000 - 210,000; Full time

Benefits offered by this job

Health insurance

Remote-work flexibility: Work remotely

Relocation support

Professional development

Career growth and mobility

Global project exposure

Inclusive, multicultural environment

Performance-driven culture

Social responsibility

Job summary

Jobgether is seeking a senior AI software engineer to develop and maintain AI agents and orchestration capabilities using Python, LangGraph, and LangChain. You will build AI-enabled guided workflows, conversational AI applications, and integrate with enterprise platforms and APIs to deliver scalable, production-ready solutions.

You will work with engineering and business stakeholders, ensure observability, testing, and performance optimization, and contribute to architecture decisions.

Qualifications

• 8+ years hands-on Python development and enterprise app engineering.
• 2+ years with LangGraph, including agent workflows, stateful orchestration, multi-agent systems, and AI-driven execution flows.
• 2+ years with LangChain, including LLM-powered apps, tool integrations, retrieval-augmented generation (RAG), and workflow automation.
• Strong understanding of agentic AI concepts, incl. planning, orchestration, memory management, and tool-calling frameworks.
• Experience building, deploying, maintaining production-grade Generative AI apps.
• Experience integrating AI solutions with enterprise platforms, backend services, and APIs.
• Designing and delivering conversational AI and chat-based apps for production environments.
• Familiarity with AI observability, monitoring, telemetry, model eval, and perf analysis tools.
• Strong knowledge of software engineering principles, automated testing, debugging, code quality, and performance optimization.
• Experience in Agile environments and cross-functional collaboration.
• Excellent analytical and problem-solving abilities and strong communication with technical and business stakeholders.
• Nice-to-have: MCP and A2A communication, multi-agent AI solutions in production, RAG architectures, cloud platforms (Azure, AWS, GCP).

Responsibilities

• Develop, enhance, and maintain AI agents and orchestration using Python, LangGraph, and LangChain.
• Build and extend AI-enabled guided workflows, intelligent automation, and enterprise AI capabilities.
• Design, implement, and optimize conversational AI applications and chat-based experiences.
• Develop agent planning, state management, orchestration, memory, tool-calling, and response-generation capabilities.
• Integrate AI agents with enterprise platforms, backend services, APIs, and external tools.
• Support AI integration frameworks and agent communication patterns for reliable interactions.
• Implement AI observability, telemetry, evaluation, and performance-tracking capabilities.
• Collaborate with engineering and business stakeholders to define technical approaches and deliver AI-powered use cases.
• Troubleshoot complex issues, improve reliability, and optimize system performance.
• Contribute to architecture discussions, reusable components, testing practices, and scalable AI development.
• Support continuous improvement of enterprise AI platform capabilities for maintainability, security, and production-readiness.
• Nice-to-have: MCP, A2A communication, multi-agent AI production deployments, and related technologies.

Skills

Python

LangGraph

LangChain

Agentic AI

Production AI apps

Agile

Communication

Problem solving

Tools

APIs

Telemetry

Testing

Job description

Accountabilities

- Develop, enhance, and maintain AI agents and orchestration capabilities using Python, LangGraph, and LangChain.

- Build and extend AI-enabled guided workflows, intelligent automation solutions, and enterprise AI capabilities.

- Design, implement, and optimize conversational AI applications and chat-based user experiences.

- Develop agent planning, state management, orchestration, memory, tool-calling, and response-generation capabilities.

- Integrate AI agents and applications with enterprise platforms, backend services, APIs, and external tools.

- Support modern AI integration frameworks and agent communication patterns to enable reliable interactions between AI systems.

- Implement AI observability, monitoring, telemetry, evaluation, and performance-tracking capabilities.

- Collaborate with engineering teams and business stakeholders to define technical approaches and deliver AI-powered business use cases.

- Troubleshoot complex technical issues, strengthen application reliability, and optimize system performance.

- Contribute to architecture discussions, engineering standards, reusable components, testing practices, and scalable AI development methodologies.

- Support the continuous improvement of enterprise AI platform capabilities, ensuring solutions remain maintainable, secure, and production-ready.

Requirements

- At least 8 years of hands-on experience in Python development and enterprise application engineering.

- At least 2 years of practical experience with LangGraph, including agent workflows, stateful orchestration, multi-agent systems, and AI-driven execution flows.

- At least 2 years of experience with LangChain, including LLM-powered applications, tool integrations, retrieval-augmented generation (RAG), and workflow automation.

- Strong understanding of agentic AI concepts, including intelligent agents, planning, orchestration, memory management, and tool-calling frameworks.

- Demonstrated experience building, deploying, and maintaining production-grade Generative AI applications.

- Experience integrating AI solutions with enterprise platforms, backend services, and APIs.

- Practical experience designing and delivering conversational AI and chat-based applications for production environments.

- Familiarity with AI observability, monitoring, telemetry, model evaluation, and performance analysis tools.

- Strong knowledge of software engineering principles, automated testing, debugging, code quality, and performance optimization.

- Experience working in Agile development environments and collaborating effectively with cross-functional teams.

- Excellent analytical and problem-solving abilities, with strong communication skills and the confidence to engage technical and business stakeholders.

- Ability to navigate complex technical challenges, take ownership of deliverables, and contribute to architectural decisions.

Nice-to-have qualifications:

- Hands-on experience with Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication.

- Experience deploying and maintaining multi-agent AI solutions in production environments.

- Knowledge of RAG architectures, enterprise knowledge systems, vector databases, and semantic search technologies.

- Familiarity with cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP).

- Experience contributing to centralized AI platforms that support multiple business functions and use cases.

- Exposure to healthcare or other highly regulated enterprise environments.

Benefits

- Competitive compensation and benefits: Access to a comprehensive compensation package, including health insurance.

- Remote-work flexibility: Work remotely from India, with the flexibility to support a distributed working environment.

- Relocation support: Access to a relocation program, subject to applicable eligibility and program conditions.

- Professional development: Opportunities to strengthen your expertise through certification programs, mentorship, and professional development initiatives.

- Career growth and mobility: Access to internal mobility opportunities and talent development programs that support long-term career progression.

- Global project exposure: Collaborate with international teams and contribute to technology initiatives for major global clients.

- Inclusive, multicultural environment: Join a diverse, collaborative team that values open communication, mutual support, team-building activities.

- Performance-driven culture: Work in an environment that emphasizes engineering excellence, accountability, continuous improvement, and successful project delivery.

- Social responsibility: Contribute to sustainability and community initiatives focused on IT education, community empowerment, fair operating practices, environmental sustainability, and gender equality.