Job Description

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

At Technoidentity, we're a Data & AI product engineering company with over 15 years of expertise in building durable digital products, intelligent enterprise solutions, and scalable Data & AI platforms. As we continue expanding globally, it's the perfect time to join our team of tech innovators and make a lasting impact.

What’s in it for You?

Technoidentity is building a high-impact Temporal Capability Center to design, deliver, and scale resilient workflow-orchestration solutions for enterprise customers. As enterprises adopt AI agents and LLM-powered applications, these systems need the same reliability, durability, and observability as any other production workflow. We are seeking a hands-on Principal AI Lead who combines deep backend and distributed-systems expertise with practical experience building AI agents, LLM applications, and production AI workflows.

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 Type Script, 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.

What Will You Be Doing?

Technical Leadership and Backend Engineering

- 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 Type Script.

- 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.

AI and Agentic Systems

- 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.

Client Engagement and Delivery

- 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, Dev Ops, security, and client teams to ensure successful implementation and production adoption.

Team and Capability Building

- 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.

RequirementsWhat Makes You the Perfect Fit?

- Bachelor's or Master's 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, Type Script. 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.

Preferred Qualifications

- 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 setting.

- 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.

Work Requirements

- 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.

Leadership Expectations

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 Measures

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.

Requirements
Machine Learning

📌 Principal AI Lead (India)
🏢 TechnoIdentity
📍 India