New opportunity

Technical Lead – Python, Agentic AI & LLM

LetzBizz · Chennai, India

About this role

Lead engineering for an enterprise SaaS product built with agentic AI and large language models. This founding technical leadership role owns platform architecture and engineering standards, guides the backend team, and remains hands-on with critical components.

Responsibilities

Translate end-to-end architecture into a delivery roadmap; design scalable backend services, APIs, data models, and integrations with enterprise systems, databases, and third-party APIs.
Architect LLM integration layers for provider abstraction, prompt versioning, caching, fallbacks, and cost control. Design agentic systems with orchestration, tool use, structured outputs, and reliability guardrails.
Develop multi-tenant architecture, access controls, auditing, and compliance readiness. Establish practices for testing, CI/CD, code reviews, and documentation; mentor engineers and lead design reviews.

Required experience and skills:

8–12 years of relevant software engineering experience, including leading backend teams of at least five engineers through design, development, and delivery. Proven delivery of multi-service platforms with defined domain boundaries.
Deep Python expertise, asynchronous programming, FastAPI or an equivalent framework, and production-scale backend development. Strong PostgreSQL and MongoDB experience covering design, optimization, and large-scale migrations.
Experience building production systems with LLMs, agentic AI, or AI orchestration frameworks, plus practical production experience with RAG, vector databases, or semantic search.
Hands-on Docker, Kubernetes, and AWS or Azure experience. Experience with multi-tenant SaaS, tenant isolation, RBAC, tenant-specific configuration, event-driven architecture, message buses, asynchronous workers, webhooks, and versioned, documented APIs.

The listed stack also includes Redis, Pydantic, MinIO/S3, LLM APIs, OpenTelemetry, Prometheus, Grafana, Git, and GitHub Actions. Immediate joiners are preferred. This is strictly office-based five days a week in Guindy / OMR, Chennai. The early-stage product offers scope to shape its architecture, engineering culture, and team, with potential growth toward a Principal or Staff Engineer role.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonFastAPIPostgreSQLMongoDBRedisDockerKubernetesPydanticMinIOAmazon S3LLM APIsOpenTelemetryPrometheusGrafanaGitGitHub ActionsAgentic AILLMsAI orchestrationRAGVector databasesSemantic searchAWSAzureAsynchronous programmingAPI designDatabase designDatabase optimizationDatabase migrationsMulti-tenant SaaS architecture for AI platforms with tenant isolation and tenant

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Turn architecture into a delivery roadmap
The posting makes the lead responsible for end-to-end architecture and a practical roadmap for a multi-service enterprise SaaS platform. It calls for clear domain boundaries and scalable services, APIs, and data models.
Build production-ready LLM and agent systems
The role owns LLM provider abstraction, prompt versioning, caching, fallbacks, and cost control, alongside agent orchestration, tool use, structured outputs, and reliability guardrails. The posting also requires production experience with RAG, vector databases, or semantic search.
Design tenant isolation and access controls
The posting explicitly assigns multi-tenant architecture, tenant-specific configuration, RBAC, auditing, and compliance readiness to this role.
Make backend data and integrations dependable
The lead will define integrations with enterprise systems, databases, and third-party APIs. Requirements also emphasize Python asynchronous backends, PostgreSQL and MongoDB design and migrations, event-driven components, and versioned, documented APIs.
Lead engineering while staying hands-on
The role combines mentoring and design reviews with testing, CI/CD, code-review, and documentation standards. The lead is also expected to write critical platform code and guide a backend team through delivery.

Likely challenges to think through

Balance agent reliability against latency and cost
Preparation hypothesis: provider fallbacks, caching, tool use, and guardrails can improve resilience but add latency, complexity, or spend. Prepare a concrete design rationale showing how to set budgets, validate structured outputs, and observe failures.
Preserve isolation without fragmenting the platform
Preparation hypothesis: tenant-specific configuration and access rules can conflict with reusable services and efficient operations. Prepare to explain where tenant boundaries are enforced, how access is tested, and what audit events are recorded.
Sequence platform work without blocking delivery
Preparation hypothesis: a founding lead may need to establish standards while shipping critical components. Prepare an example of phasing architecture decisions, delegating design work, and reserving hands-on effort for the highest-risk code.
Manage change across data stores and integrations
Preparation hypothesis: large-scale migrations, asynchronous workers, webhooks, and enterprise integrations create risks around compatibility and partial failure. Prepare to discuss versioning, retry and idempotency choices, migration rollout, and recovery trade-offs.

Practice your answers

Technical practice

How would you turn an enterprise SaaS architecture into a delivery roadmap?

What to coverSuggested preparation: Outline domain boundaries, service ownership and the order in which core capabilities would be delivered. Explain how you would balance a workable first release against scalability and integration needs.

How would you design a production Python API that performs concurrent work?

What to coverSuggested preparation: Discuss asynchronous programming in FastAPI or an equivalent framework, including where blocking work belongs and how you would control concurrency. Explain how you would test performance and diagnose a slow endpoint.

How would you design and migrate data across PostgreSQL and MongoDB?

What to coverSuggested preparation: Explain how access patterns and consistency needs inform data models and database choice. Cover query optimisation and a large-scale migration approach that limits disruption and verifies data correctness.

How would you build an LLM integration layer that can evolve across providers?

What to coverSuggested preparation: Address provider abstraction, prompt versioning, caching, fallbacks and cost control, all named in the role. Discuss the trade-offs between response quality, latency, reliability and spend.

How would you make an agentic workflow reliable when it uses external tools?

What to coverSuggested preparation: Describe orchestration, tool selection, structured-output validation and guardrails around failed or unexpected tool responses. Explain how you would bound retries and evaluate whether the workflow completes its intended task.

How would you evaluate a production RAG or semantic-search system?

What to coverSuggested preparation: Walk through retrieval and answer-quality checks, including how you would investigate poor results. Compare relevant trade-offs in indexing, freshness and latency without assuming a particular vector database.

How would you enforce tenant isolation in a multi-tenant AI SaaS platform?

What to coverSuggested preparation: Cover tenant-aware data access, RBAC, tenant-specific configuration and auditing. Explain how you would test isolation across APIs, background work and AI integrations, and what evidence would support compliance readiness.

How would you integrate enterprise systems using events, workers and webhooks?

What to coverSuggested preparation: Describe when to use an API call versus a message bus or asynchronous worker, and how you would handle retries and duplicate events. Include how third-party failures and webhook delivery would affect the integration design.

How would you design APIs and data models shared by multiple teams?

What to coverSuggested preparation: Discuss clear contracts, versioning, documentation and compatibility during change. Explain how design reviews would expose domain-boundary and integration trade-offs before implementation.

How would you deploy and observe critical backend services?

What to coverSuggested preparation: Relate Docker, Kubernetes and AWS or Azure to deployment and recovery choices, without assuming a particular cloud setup. Discuss what you would instrument with the listed observability stack and how findings would guide troubleshooting.

Behavioural practice

Tell me about a time you converted a broad architecture into an achievable delivery plan.

What to coverSuggested preparation: Give the situation, your decisions about priorities and domain boundaries, and how you aligned the team. Describe the delivery result and what you changed when constraints emerged.

Tell me about a time you led backend engineers through a difficult design decision.

What to coverSuggested preparation: Describe the situation, competing proposals and how you ran the review or resolved disagreement. State what the team implemented and the result, including any trade-off you would revisit.

Tell me about a time you mentored an engineer while maintaining delivery momentum.

What to coverSuggested preparation: Identify the situation, the support or feedback you provided, and how you balanced coaching with project needs. Give an observable result without claiming credit for someone else's work.

Tell me about a time you improved engineering standards across a team.

What to coverSuggested preparation: Use a real situation involving testing, CI/CD, code reviews or documentation. Explain the actions you took to secure adoption and the resulting change in delivery or code quality.

Tell me about a time you stayed hands-on with a critical component while leading a team.

What to coverSuggested preparation: Describe why that component needed your direct involvement, what you built or debugged, and how you avoided becoming a bottleneck. Give the result and explain how you handed off knowledge or ownership.

Sharpen your knowledge

Topics to prepare

Multi-service architecture and delivery roadmap
The posting requires ownership of platform architecture, defined domain boundaries and a practical delivery roadmap. For revision, sketch service boundaries for an enterprise SaaS platform, then sequence their delivery and explain the trade-offs in a design review.
LLM integration and agent reliability
The role covers provider abstraction, prompt versioning, caching, fallbacks, cost control, orchestration, tool use and structured outputs. For revision, diagram a request flow and explain how it handles a provider failure, an invalid tool result and a cost limit.
Production retrieval design
Production experience with RAG, vector databases or semantic search is required. For revision, compare retrieval and answer-generation stages, then outline how you would check answer relevance and diagnose poor retrieval. This is a preparation exercise, not a prescribed company design.
Multi-tenant access and audit design
The posting specifies tenant isolation, RBAC, tenant-specific configuration, auditing and compliance readiness. For revision, trace a request across API, service and data layers, marking where tenant identity and permissions must be enforced and recorded.
Event-driven integrations and API contracts
The role calls for message buses, asynchronous workers, webhooks, enterprise integrations and versioned, documented APIs. For revision, map an integration event from receipt to processing and explain how retries, duplicate delivery and API version changes would be handled.
Data design and migration trade-offs
Strong PostgreSQL and MongoDB experience, including design, optimisation and large-scale migrations, is required. For revision, compare where each database fits in a sample multi-service data model and outline a migration with validation and rollback considerations.

Skills to revise

Build an asynchronous Python API slice
The posting requires deep Python, asynchronous programming and FastAPI or an equivalent framework. As practice, implement a small versioned endpoint with Pydantic validation, an asynchronous integration call and tests for errors and timeouts.
Rehearse a database migration
PostgreSQL and MongoDB design, optimisation and migrations are explicit requirements. As practice, write a small schema or document change, test reads during the transition and inspect a representative query before and after optimisation.
Prototype an LLM integration path
The role remains hands-on with critical components and specifies LLM APIs and agentic systems. As practice, implement a narrow tool-use flow with a structured output, versioned prompt, fallback and tests for malformed responses; treat this as revision, not a stated project.
Implement an event and webhook workflow
The posting calls for asynchronous workers, message buses and webhooks. As practice, implement a small event consumer and webhook handler, then test duplicate messages, failed processing and retry behaviour.
Exercise deployment and observability
Hands-on Docker, Kubernetes and AWS or Azure experience is required; OpenTelemetry, Prometheus and Grafana are listed in the stack. As practice, containerise a small service, prepare a Kubernetes deployment and identify traces and metrics that would reveal latency or failures.
Run a design-and-review exercise
The lead is expected to guide a backend team, conduct design reviews and establish testing, CI/CD, code-review and documentation standards. As practice, write a brief design proposal and review checklist, then explain how you would turn review feedback into delivery steps; GitHub Actions is listed in the stack.

Your preparation checklist

Build a platform architecture walkthrough
Prepare a diagram and short narrative for a multi-service platform: domain boundaries, backend services, data models and a phased delivery roadmap. Use a real project if available; label any hypothetical design as an exercise.
Prepare a hands-on Python and data example
Be ready to explain an asynchronous Python service, its API design, and a PostgreSQL or MongoDB design, optimization or migration decision. Bring specific code or measurable outcomes only if you can substantiate them.
Practice an LLM and agent design exercise
Sketch provider abstraction, prompt versioning, caching, fallbacks and cost controls alongside tool use, structured outputs and reliability guardrails. Prepare to discuss any production LLM or RAG example you have; otherwise keep the sketch explicitly hypothetical.
Map tenant boundaries and access controls
Draw how an enterprise SaaS design could handle tenant isolation, RBAC, tenant-specific configuration and audit records. Note the security trade-offs and what would need validation before implementation.
Explain integration and delivery patterns
Prepare an API and integration sketch covering versioning, documentation, enterprise or third-party systems, message buses, asynchronous workers and webhooks. Identify failure handling and ownership boundaries.
Gather evidence of production engineering practices
Organize examples, where available, of Docker, Kubernetes and AWS or Azure work, plus testing, CI/CD, observability, code reviews and documentation. Distinguish what you personally implemented from team decisions.
Rehearse a technical leadership story
Prepare one walkthrough of how you led backend engineers through design, development and delivery, including design reviews, mentoring, a difficult trade-off and your hands-on contribution. The posting specifies teams of at least five engineers.
Write questions for the employer
Ask about the current platform stage, first architecture and delivery priorities, AI reliability expectations, tenant and compliance needs, and team decision-making. Clarify the five-day office requirement in Guindy / OMR and the immediate-joiner preference if relevant.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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