Location: Hyderabad, India (Hybrid; subject to confirmation)
Experience: 18+ years in software engineering, with significant experience in engineering leadership and solution architecture
Role Type: Engineering Leadership with Strategic Architecture and Hands-on Technical Oversight

Role Overview

We are looking for an experienced Associate Director / Engineering Director – Full Stack AI Solutions to lead the architecture, engineering, and delivery of enterprise-grade AI-enabled product capabilities.

The ideal candidate will bring a strong combination of engineering leadership, full-stack development expertise, enterprise solution architecture, and practical experience in agentic AI, API integration, knowledge graphs, and data-driven applications.

This role will be responsible for defining the technical direction of AI-enabled products, establishing engineering standards, guiding architecture and design decisions, and ensuring the delivery of scalable, secure, reliable, and maintainable solutions. The candidate will work closely with business stakeholders, product leaders, domain experts, and cross-functional engineering teams to translate business objectives into executable technology strategies.

The position requires a leader who can operate at both strategic and technical levels—driving engineering excellence and team effectiveness while remaining sufficiently hands-on to evaluate architecture decisions, guide complex implementations, review critical code, and resolve high-impact technical challenges.

The focus is on engineering strategy, architecture ownership, technical governance, and successful product engineering, rather than project management alone.

Key Responsibilities

1. Engineering Strategy and Technical Leadership

Define and drive the engineering strategy, technical roadmap, and architectural direction for AI-enabled enterprise products.
Establish engineering principles, reference architectures, design standards, and implementation practices across frontend, backend, AI, data, and integration layers.
Provide technical leadership across multiple engineering workstreams, ensuring consistency, scalability, maintainability, and alignment with product objectives.
Evaluate emerging technologies, frameworks, and architectural approaches to determine their suitability for enterprise AI applications.
Make and govern critical technical decisions involving system design, scalability, reliability, performance, security, and cost optimization.
Identify architectural risks, technology dependencies, integration challenges, and technical debt; establish mitigation strategies and long-term improvement plans.
Balance speed of delivery with engineering quality, platform sustainability, and production readiness.

2. Enterprise Solution Architecture

Own the end-to-end architecture of complex AI-enabled applications spanning frontend experiences, backend services, APIs, AI orchestration, data platforms, and enterprise systems.
Translate business objectives and domain requirements into solution architectures, technical specifications, integration patterns, and implementation strategies.
Define architectural patterns for distributed systems, API-first applications, event-driven services, data integration, and AI-enabled workflows.
Design and govern integration with enterprise APIs, including OData services, entity models, query semantics, and data access patterns.
Guide the integration of knowledge graphs, semantic layers, graph databases, and structured and unstructured enterprise data.
Ensure architectures meet enterprise requirements for security, identity and access management, data governance, observability, resilience, and regulatory compliance.
Lead architecture reviews and technical assessments to validate design decisions and ensure solutions are fit for production.

3. Agentic AI and Intelligent Application Engineering

Define the architecture and engineering approach for agentic AI solutions, including orchestration, tool and function calling, retrieval, memory, evaluation, and guardrails.
Assess and guide the use of single-agent, multi-agent, and human-in-the-loop patterns based on business requirements, risk, and operational complexity.
Establish engineering standards for integrating AI models and agents with enterprise applications, APIs, knowledge sources, and business workflows.
Guide the development of reliable AI-enabled capabilities with appropriate grounding, evaluation, traceability, and fallback mechanisms.
Oversee approaches for managing hallucinations, data quality issues, model uncertainty, latency, operating cost, and other production risks.
Partner with AI and data engineering specialists to improve solution quality, performance, scalability, and operational reliability.
Evaluate AI frameworks, model integration patterns, and orchestration technologies based on enterprise suitability and long-term maintainability.

4. Full-Stack Product Engineering

Provide technical direction across frontend applications, backend services, APIs, data services, and AI-powered user experiences.
Guide the development of responsive, accessible, performant, and maintainable user interfaces integrated with enterprise services and AI capabilities.
Oversee backend service design, API contracts, application integration, data modeling, and end-to-end feature implementation.
Drive sound software engineering practices, including modular design, separation of concerns, reusable components, automated testing, and maintainable codebases.
Review critical designs and implementations, contribute to complex technical problem-solving, and provide hands-on guidance where necessary.
Ensure engineering teams can adapt to evolving product requirements, user experience standards, and changing technical constraints without compromising quality.

5. Engineering Management and Team Development

Lead, mentor, and develop cross-functional engineering teams comprising frontend, backend, AI, data, and quality engineering specialists.
Establish clear technical ownership, engineering accountability, and collaboration practices across teams and workstreams.
Coach senior engineers and technical leads in architecture, design decisions, problem-solving, and engineering leadership.
Define and promote standards for code quality, code reviews, design reviews, testing, documentation, and technical decision-making.
Support workforce planning, skills assessment, technical hiring, onboarding, and capability development as required.
Foster a culture of continuous improvement, engineering discipline, knowledge sharing, and collaborative problem-solving.
Build effective working relationships with product management, business stakeholders, and subject-matter experts.

6. Delivery Governance and Production Excellence

Establish technical governance mechanisms to monitor architecture quality, implementation progress, technical risks, and production readiness.
Work with product and delivery leaders to translate business priorities into technical roadmaps, milestones, and engineering execution plans.
Ensure appropriate engineering practices across CI/CD, automated testing, application security, monitoring, logging, and incident management.
Drive performance optimization, reliability improvements, technical debt reduction, and operational readiness.
Define and monitor relevant engineering metrics, such as defect trends, service reliability, deployment quality, performance, and engineering efficiency.
Ensure technical decisions support sustainable product evolution, rather than short-term delivery at the expense of long-term maintainability.

Required Skills and Experience

12–18+ years of professional software engineering experience, including substantial ownership of complex enterprise applications.
Significant experience in engineering leadership, technical architecture, or leading multiple engineering teams or workstreams.
Strong expertise in full-stack application architecture, covering frontend frameworks, backend services, APIs, data integration, and distributed systems.
Practical experience designing or building agentic AI systems or enterprise AI-enabled applications.
Strong understanding of agent orchestration, tool/function calling, retrieval-augmented generation (RAG), evaluation, and guardrail patterns.
Experience designing, modeling, querying, or integrating knowledge graphs, graph databases, ontologies, or semantic layers.
Experience designing or integrating OData APIs, including entity models, query behavior, and enterprise data access patterns.
Strong understanding of software architecture, design patterns, microservices or comparable distributed architectures, and API-first development.
Experience defining engineering standards, conducting architecture reviews, managing technical risks, and guiding complex implementation decisions.
Knowledge of application security, identity and access management, observability, performance engineering, and production-readiness practices.
Experience mentoring senior engineers and technical leads and improving engineering effectiveness across teams.
Ability to communicate architectural trade-offs and technical strategy clearly to executive stakeholders, product leaders, engineers, and domain experts.
Proven ability to evaluate unfamiliar technology environments and guide teams through evolving product and platform requirements.

Preferred Qualifications

Experience leading engineering for enterprise AI products or production-grade AI-enabled platforms.
Cloud architecture experience across Azure, AWS, GCP, or comparable enterprise cloud environments.
Experience with enterprise data platforms, event-driven architectures, and complex system integrations.
Familiarity with enterprise architecture governance, platform engineering, DevSecOps, and software delivery maturity models.
Experience designing AI-powered user experiences, including streaming responses, workflow automation, and human-in-the-loop processes.
Experience defining engineering metrics, technical roadmaps, architecture standards, and technology modernization strategies.
Experience working with globally distributed teams and enterprise customers in complex technology environments.
Relevant architecture, cloud, AI, or engineering leadership certifications are an advantage.

Expected Outcomes

A clear, scalable, and maintainable technical architecture aligned with product and business objectives.
Consistent engineering standards and architecture governance across product engineering teams.
Reliable integration across frontend applications, backend services, AI components, APIs, and enterprise data platforms.
Production-ready agentic AI capabilities with appropriate evaluation, security, observability, and guardrails.
Improved engineering quality, system reliability, performance, and maintainability.
Reduced architectural risk and technical debt through proactive design reviews and engineering governance.
Stronger engineering capability through effective technical leadership, mentoring, and team development.
Predictable, high-quality engineering execution aligned with product priorities and long-term platform evolution.

Ideal Candidate Profile

An accomplished engineering leader who has progressed beyond individual feature ownership to leading complex systems, setting technical direction, and guiding multiple engineers or teams. The candidate combines deep software engineering fundamentals with enterprise architecture expertise and a practical understanding of agentic AI and intelligent applications.

The ideal candidate can define an end-to-end technical strategy, challenge architectural assumptions, evaluate trade-offs, anticipate production failure modes, and guide teams in translating complex designs into secure, scalable implementations. They should be comfortable engaging with senior stakeholders while retaining sufficient technical depth to review architecture, guide critical implementations, and make informed engineering decisions.

Success in this role requires the ability to connect business objectives with engineering strategy, build strong technical teams, and deliver enterprise-grade AI solutions at scale.

Associate Director - AI Native Solutions