Principal AI Lead

Supercharge Your Career as a Principal AI Lead Developer at Technoidentity!

Whats in it for You

This role suits a resourceful engineer who can independently take multi-faceted business and technical requirements from discovery through design implementation rollout and operational improvement. You will work directly with overseas clients shape architecture decisions lead multiple technical workstreams and mentor engineers while remaining actively involved in code design reviews and delivery.

The ideal candidate has strong practical experience in Python Java and/or TypeScript has worked with cloud platforms and has built LLM-powered or agentic applications that run in production. Experience with workflow orchestration platforms such as Temporal Cadence Camunda or similar technologies is preferred but is not a requirement.

• Lead the end-to-end technical delivery of workflow-orchestration distributed-systems and AI-enabled solutions for enterprise clients.
  
• Design robust scalable secure and observable architectures using durable execution asynchronous messaging APIs event-driven patterns and cloud-native components.
  
• Build production-grade services and workflow implementations using Python Java and/or TypeScript.
  
• Design and implement long-running business processes using orchestration frameworks such as Temporal Cadence Camunda or equivalent platforms.
  
• Apply distributed-systems principles to address reliability idempotency retries timeouts compensating transactions eventual consistency failure recovery and data consistency.
  
• Design and implement Saga patterns including orchestration-based and choreography-based approaches for multi-service business transactions.
  
• Establish engineering standards for workflow design API contracts error handling versioning testing deployment observability and operational readiness.
  
• Perform hands-on development of critical components prototypes integrations and reference implementations.
  
• Conduct code reviews and architectural reviews ensuring solutions are maintainable performant secure and aligned with engineering best practices.
  

• Design develop and maintain AI/ML applications and LLM-powered solutions that solve real business problems.
  
• Architect and build AI agents agentic workflows and orchestration layers including coordination of multiple agents tool and API integrations workflow state management guardrails and human-in-the-loop controls.
  
• Develop and integrate Model Context Protocol (MCP) tools and other AI system integrations.
  
• Apply prompt engineering retrieval-augmented generation (RAG) embeddings function calling and workflow automation patterns to production use cases.
  
• Define how model performance reliability safety and output quality are evaluated and build evaluation pipelines that run before and after release.
  
• Own production deployment monitoring and continuous improvement of AI systems applying MLOps and LLMOps practices and observability for AI workloads.
  
• Make AI workloads durable by combining them with workflow orchestration so that long-running agent tasks recover from failures retries and timeouts.
  
• Document solution architecture workflows evaluation results and implementation details.
  

• Lead architecture discovery sessions technical workshops solution presentations and design reviews with overseas clients and internal stakeholders.
  
• Translate business requirements including AI use cases into clear solution architectures implementation plans technical specifications estimates and delivery milestones.
  
• Drive multiple concurrent technical streams identify dependencies and risks early and maintain delivery quality under changing priorities.
  
• Collaborate with delivery product platform QA DevOps security and client teams to ensure successful implementation and production adoption.
  

• Use AI-assisted SDLC practices responsibly to improve developer productivity code quality test coverage documentation and delivery velocity.
  
• Mentor and guide engineers including AI/ML engineers and junior developers through pairing reviews reusable patterns technical sessions and constructive feedback.
  
• Contribute reusable assets accelerators templates documentation and best practices to the Temporal Capability Center.
  

• Bachelors or Masters degree in Computer Science IT Engineering Data Science or a related technical discipline.
  
• 5 years of professional backend software-development experience including experience leading technical delivery and mentoring engineering teams.
  
• 3 years of hands-on experience in AI/ML engineering LLM application development or applied AI including building LLM-powered applications or AI agents.
  
• Strong hands-on proficiency in at least one of the following languages: Python Java TypeScript. Python experience with common AI/ML and LLM development frameworks is required.
  
• Demonstrated experience designing and building distributed scalable highly available or event-driven systems.
  
• Strong understanding of microservices architecture REST and/or asynchronous APIs message-driven systems and integration patterns.
  
• Practical expertise with distributed-systems concerns including:
  
  • Idempotency and duplicate-message handling
    
  • Retries backoff timeouts and circuit-breaking strategies
    
  • Failure handling and recovery
    
  • Eventual consistency and data synchronization
    
  • State management for long-running processes
    
  • Observability logging metrics tracing and operational debugging
    
• Strong understanding and practical application of the Saga pattern and compensating-transaction design.
  
• Working knowledge of AI agent development orchestration and tool-based workflows.
  
• Understanding of prompt engineering RAG function calling embeddings and evaluation techniques.
  
• Ability to deploy monitor and improve AI solutions in production environments with an understanding of MLOps LLMOps observability and evaluation pipelines.
  
• Exposure to Model Context Protocol (MCP) tools and integrations or willingness to learn quickly.
  
• Experience designing solution architectures and communicating technical trade-offs to both engineering and business stakeholders.
  
• Ability to lead engineers through technical direction task decomposition code review and mentorship.
  
• Strong written and verbal communication skills in English.
  
• High ownership self-motivation adaptability and the ability to make sound decisions in ambiguous and fast-moving environments.
  

• Production experience with Temporal Cadence Camunda AWS Step Functions Azure Durable Functions Netflix Conductor Apache Airflow or comparable orchestration technologies.
  
• Experience with agent development frameworks such as ADK and with agent-to-tool integrations through MCP.
  
• Experience with vector databases such as Pinecone Weaviate Qdrant Chroma or pgvector.
  
• Experience migrating from legacy schedulers BPM platforms or synchronous microservice flows to durable workflow orchestration.
  
• Practical use of AI coding assistants and AI-aided SDLC workflows for planning implementation testing code review documentation and troubleshooting.
  
• Cloud-native development experience on AWS Azure Google Cloud Platform or a hybrid-cloud environment.
  
• Experience with Kubernetes Docker CI/CD pipelines Infrastructure as Code and container-based deployments.
  
• Experience with event-streaming and messaging platforms such as Kafka RabbitMQ cloud queues or pub/sub systems.
  
• Experience with relational and NoSQL databases data-modeling strategies and transactional/outbox patterns.
  
• Experience with API gateways security controls OAuth/OIDC secrets management and secure service-to-service communication.
  
• Familiarity with domain-driven design event sourcing CQRS and enterprise integration patterns.
  
• Exposure to enterprise workflows or regulated production environments.
  
• Experience in technical consulting pre-sales support solution accelerators or client-facing technical leadership.
  

• Willingness to work flexible shift timings based on project requirements.
  
• Ability to work independently on POCs prototypes and production AI solutions.
  
• Strong ownership in building reliable secure and useful AI systems.
  

As a Principal AI Lead you will be expected to:

• Act as a trusted technical advisor to clients and internal delivery teams on both orchestration and AI.
  
• Balance architecture leadership with hands-on engineering execution.
  
• Proactively identify problems propose practical solutions and drive them to completion without waiting for detailed direction.
  
• Manage and prioritize several workstreams while communicating progress risks decisions and dependencies clearly.
  
• Raise engineering quality through reusable standards design patterns code reviews and mentoring.
  
• Build confidence with clients through technical depth clarity responsiveness and reliable delivery.
  
• Foster a collaborative accountable learning-oriented engineering culture within the Temporal Capability Center.
  

Success in this role will be measured by:

• Delivery of reliable scalable and maintainable orchestration and AI solutions that solve real client problems.
  
• Quality of solution architecture code technical documentation and operational readiness.
  
• Effective application of workflow-orchestration and Saga patterns to complex distributed business processes.
  
• Production AI systems that meet agreed standards for reliability safety and output quality backed by evaluation results.
  
• Ability to independently lead client-facing technical discussions and turn them into executable delivery plans.
  
• Delivery predictability across multiple concurrent technical initiatives.
  
• Measurable improvement in engineering productivity and quality through AI-assisted SDLC practices.
  
• Growth engagement and technical effectiveness of engineers under your guidance.
  
• Contribution of reusable Temporal Capability Center assets reference architectures and best practices.