Team Lead and Developer

Bobton Compliance Solutions; Hyderabad; On-site; INR 420,000 - 700,000; Full time

Job summary

Bobton Compliance Solutions seeks an experienced Technical Lead for full‑stack, cloud and agentic AI engineering. You will guide architecture, lead a small team, and contribute hands‑on to design, code and reviews.

This long‑term leadership role spans web, mobile, backend and cloud initiatives with a focus on scalable, secure delivery. You will establish engineering standards, governance and reusable foundations while enabling growth across products and client initiatives.

Qualifications

• Bachelor’s degree in Computer Science, Engineering or related discipline, or equivalent practical experience.
• 8+ years of software engineering experience, including at least 3 years in a Technical Lead, Lead Engineer, Solution Architect or equivalent hands‑on leadership role.
• Deep production experience in at least one major backend stack such as .NET/C#, Java/Spring, Node.js/TypeScript, Python/FastAPI/Django or Go.
• Strong experience with at least one modern frontend ecosystem such as React, Angular or Vue, including component design, state management, testing and performance.
• Strong understanding of API design, relational and non‑relational databases, caching, messaging, asynchronous processing and distributed‑system fundamentals.
• Production experience on at least one major cloud platform: AWS, Microsoft Azure or Google Cloud.
• Working knowledge of cloud‑neutral concepts including containers, Kubernetes or managed container platforms, serverless computing, identity, networking, storage, observability, infrastructure as code and cost management.
• Practical experience integrating LLMs or generative‑AI capabilities into applications, including prompt/context engineering, RAG, tool calling, structured outputs, guardrails and evaluation.

Responsibilities

• Technology strategy and architecture: Define solution architecture, technology standards, integration patterns and technical roadmaps across web, mobile, backend, cloud, data and AI systems.
• Stack evaluation: Evaluate and choose among major programming ecosystems such as JavaScript/TypeScript, .NET, Java, Python and Go based on the problem being solved.
• Cloud leadership: Design secure, scalable and cost‑aware solutions on at least one leading cloud platform—AWS, Azure or Google Cloud—and guide cross‑cloud decisions when required.
• Hands‑on engineering: Design and implement components, prototypes and reference patterns; review PRs and resolve complex issues.
• Agentic AI development: Lead the design of AI agents, tool usage, retrieval‑augmented generation, memory, planning and multi‑agent collaboration.
• AI interoperability: Guide MCP and A2A integrations while ensuring authentication, permissions, auditability and safe tool execution.
• AI orchestration and workflows: Evaluate LangChain, LangGraph, CrewAI, Semantic Kernel, AutoGen, n8n, Temporal, Airflow, Prefect and cloud‑native services.
• AI‑assisted development: Establish safe usage practices for GitHub Copilot, Claude Code, OpenAI Codex, Cursor and comparable agents.
• Engineering quality: Establish coding standards, automated testing, CI/CD, code scanning and release controls.

Skills

JavaScript/TypeScript

Java/Spring

Python/FastAPI/Django

Go

React/Angular/Vue

API design

CI/CD

Git-based development

Security practices

Education

Bachelor's degree in Computer Science or related field

Tools

AWS

Azure

Google Cloud

Docker

Kubernetes

Terraform/Pulumi

LangChain / Semantic Kernel

Job description

TECHNICAL LEAD FULL STACK, CLOUD & AGENTIC AI ENGINEERING

Full-Time, Permanent Employment

Department

Engineering / Product & Technology

Employment Type

Full-time, Permanent

Reports To

CTO / Head of Engineering / Founder

Initial Team

Leads 2–3 AI-enabled developers

Location

[Onsite / Hybrid / Remote – confirm]

Experience

8+ years overall; 3+ years in technical leadership

Role Level

Technical Lead / Lead Engineer

Compensation

Competitive; based on experience and role fit

1. Job Summary

We are hiring a hands‑on Technical Lead to guide the design and delivery of enterprise software, cloud platforms and AI‑enabled solutions across multiple products and client initiatives. This is a long‑term engineering leadership role, not a position tied to one project or one technology stack. The successful candidate will initially lead a focused team of 2–3 AI-enabled developers, contribute directly to architecture and code, establish engineering standards, and help scale the team as the business grows.

Confidentiality: Detailed product concepts, client information, business models and roadmaps will be shared only at the appropriate stage of the hiring process and under applicable confidentiality controls.

2. Role Purpose

• Own technical direction across enterprise web, mobile, backend, cloud, data and AI initiatives.
• Select suitable technologies based on business goals, team capability, security, scalability, cost and delivery timelines—not personal preference for one stack.
• Lead a small engineering team while remaining hands‑on in solution design, coding, reviews, testing, releases and production support.
• Create reusable engineering foundations, standards and delivery practices that can support multiple products and future team expansion.
• Apply AI‑assisted engineering responsibly to improve delivery speed while preserving security, maintainability and human accountability.

3. Key Responsibilities

• Technology strategy and architecture: Define solution architecture, technology standards, integration patterns and technical roadmaps across web, mobile, backend, cloud, data and AI systems.
• Stack evaluation: Evaluate and choose among major programming ecosystems such as JavaScript/TypeScript, .NET, Java, Python and Go based on the problem being solved.
• Cloud leadership: Design secure, scalable and cost‑aware solutions on at least one leading cloud platform—AWS, Microsoft Azure or Google Cloud—and guide cross‑cloud decisions when required.
• Hands‑on engineering: Design and implement critical components, prototypes and reference patterns; review pull requests and resolve complex technical issues.
• Agentic AI development: Lead the design of AI agents, tool‑using systems, retrieval‑augmented generation, memory, planning, multi‑agent collaboration, human‑in‑the‑loop controls and agent evaluation.
• AI interoperability: Guide integrations based on emerging standards such as Model Context Protocol (MCP) and Agent2Agent (A2A), while ensuring authentication, permissions, auditability and safe tool execution.
• AI orchestration and workflows: Evaluate frameworks and platforms such as LangChain, LangGraph, CrewAI, Semantic Kernel, AutoGen, n8n, Temporal, Airflow, Prefect and cloud‑native workflow services.
• AI‑assisted development: Establish safe and effective usage practices for tools such as GitHub Copilot, Claude Code, OpenAI Codex, Cursor and comparable coding agents.
• Engineering quality: Establish coding standards, architecture decision records, automated testing, CI/CD, code scanning, release controls, observability, incident response and rollback practices.
• Security and governance: Apply identity and access management, secure coding, secrets management, privacy, data protection, threat modelling, AI guardrails and responsible‑AI controls.
• Team leadership: Plan work, assign ownership, mentor developers, remove blockers, conduct reviews and build a high‑accountability engineering culture.
• Stakeholder communication: Explain technical options, risks, trade‑offs, dependencies and progress clearly to business, product, operations and client stakeholders.
• Scalability and reuse: Identify shared components, services, libraries, agent capabilities and platform patterns that can be reused across multiple products.

4. Required Experience and Qualifications

• Bachelor’s degree in Computer Science, Engineering or a related discipline, or equivalent practical experience.
• 8+ years of software engineering experience, including at least 3 years in a Technical Lead, Lead Engineer, Solution Architect or equivalent hands‑on leadership role.
• Deep production experience in at least one major backend stack, such as .NET/C#, Java/Spring, Node.js/TypeScript, Python/FastAPI/Django or Go.
• Strong experience with at least one modern frontend ecosystem, such as React, Angular or Vue, including component design, state management, testing and performance.
• Strong understanding of API design, relational and non‑relational databases, caching, messaging, asynchronous processing and distributed‑system fundamentals.
• Production experience on at least one major cloud platform: AWS, Microsoft Azure or Google Cloud.
• Working knowledge of cloud‑neutral concepts including containers, Kubernetes or managed container platforms, serverless computing, identity, networking, storage, observability, infrastructure as code and cost management.
• Practical experience integrating LLMs or generative‑AI capabilities into applications, including prompt/context engineering, RAG, tool calling, structured outputs, guardrails and evaluation.
• Understanding of agent design patterns, orchestration, state and memory, human approval, failure handling, observability and secure tool access.
• Experience with Git‑based development, pull requests, branching, CI/CD, automated testing, security scanning and production release management.
• Strong knowledge of application security, authentication, authorization, OWASP practices, secrets handling, audit logging and data protection.
• Ability to lead a small team while remaining accountable for architecture, code quality, delivery predictability and production outcomes.
• Strong written and verbal communication skills, including the ability to explain technical decisions in clear business language.

5. Agentic AI and Emerging Technology Expectations

The candidate is not expected to have used every framework listed below. The requirement is strong practical understanding of agentic systems, proven depth in selected tools, and the ability to evaluate new technologies objectively.

Capability Area

Expected Knowledge

Representative Technologies

Agent architecture

Single‑agent and multi‑agent patterns; tool use; planning; state; memory; delegation; human approval; retries and fallback.

LangGraph, CrewAI, Semantic Kernel, AutoGen, custom agent runtimes

Context and retrieval

Document ingestion, embeddings, vector search, hybrid retrieval, metadata filters, grounding, citations and tenant/data boundaries.

RAG, vector databases, enterprise search, reranking and evaluation tools

Interoperability

Standardised connections between models, tools, data sources and other agents; secure capability discovery and invocation.

MCP, A2A, APIs, event‑driven integration

Evaluation and operations

Quality metrics, test datasets, tracing, cost/latency monitoring, hallucination controls, prompt/version management and production observability.

LangSmith, OpenTelemetry, cloud AI observability, custom evaluation pipelines

Workflow automation

Combining deterministic workflows with agentic decision‑making and approval checkpoints.

n8n, Temporal, Airflow, Prefect, cloud‑native workflow services

AI‑assisted SDLC

Using coding agents for planning, implementation, testing, documentation, review and refactoring under governance controls.

GitHub Copilot, Claude Code, OpenAI Codex, Cursor and comparable tools

6. Preferred Skills and Experience

• Experience designing multi‑tenant SaaS platforms, enterprise integration platforms or security‑sensitive systems.
• Experience across more than one cloud provider or with cloud migration and portability decisions.
• Experience with mobile development using React Native, Flutter, native Android/iOS or hybrid app‑shell approaches.
• Experience with Kubernetes, service meshes, event‑driven architecture, workflow engines or high‑scale distributed systems.
• Experience with vector databases, enterprise search, knowledge graphs, document intelligence or multimodal AI.
• Experience with LLMOps, model gateways, prompt/version management, AI evaluation, red‑teaming and responsible‑AI governance.
• Experience with data engineering, streaming, analytics, ML pipelines or MLOps.
• Experience with infrastructure as code using Terraform, Pulumi, CloudFormation, Bicep or equivalent tooling.
• Experience in regulated, financial, healthcare, identity, cybersecurity or other security‑sensitive environments.
• Relevant cloud, architecture, security, Kubernetes or AI certifications are beneficial but not mandatory.

7. Leadership and Behavioural Competencies

Competency

Expected Behaviour

Technical judgement

Chooses technologies based on evidence, constraints and long‑term maintainability rather than trends alone.

Ownership

Takes responsibility for technical decisions, delivery quality, security and production outcomes.

Hands‑on leadership

Can move between architecture, implementation, debugging, code review and mentoring as required.

Learning agility

Continuously evaluates emerging AI, cloud and engineering practices and separates useful advances from hype.

Coaching

Develops team capability through clear feedback, pairing, reviews, examples and structured guidance.

Communication

Communicates risks, trade‑offs and progress clearly without unnecessary jargon.

Pragmatism

Balances speed, quality, cost, security and business value; avoids unnecessary complexity.

8. Initial Team Context

• Lead an initial engineering team of 2–3 AI‑enabled developers.
• Remain hands‑on while establishing technical standards, delivery controls and reusable foundations.
• Help define future hiring priorities and team structure as products and customer demand expand.
• Support multiple product and client initiatives rather than operating as a project‑specific contractor.

9. Success Measures

Period

Expected Outcome

First 30 days

Understand priorities and team capability; validate the engineering approach; establish delivery, security and AI‑development guardrails.

First 60–90 days

Deliver a production‑quality vertical slice or major release increment with automated testing, CI/CD, observability and clear technical documentation.

First 6 months

Create stable, reusable engineering foundations; improve delivery predictability; mentor the initial team; and establish a scalable cloud and AI engineering approach.

Ongoing

Improve reliability, security, engineering quality, team capability, cost efficiency and stakeholder confidence across initiatives.

10. Growth and Development Opportunity

This is a foundational engineering leadership role with the opportunity to shape technology strategy, engineering culture, AI adoption, delivery practices and future technical hiring. As the organisation grows, the role may progress toward Principal Engineer, Engineering Manager, Head of Engineering, Chief Architect or a broader technology leadership position, depending on performance and organisational needs.

11. Compensation and Benefits

• Competitive full‑time compensation based on experience and role fit.
• Performance‑linked bonus or benefits, subject to company policy.
• Learning, certification and professional development support, subject to approval.
• Leave, insurance and other employee benefits according to company policy and applicable employment requirements.