Forward Deployed Software Engineer

Hack And Grow Consultancy; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Hack And Grow Consultancy seeks a Technical Architect - AI & Innovation in Bengaluru to shape enterprise-grade AI architectures, spanning model access, agent orchestration, tools, APIs, and security. You will balance rapid experimentation with secure, reusable, enterprise-grade solutions.

Lead architecture reviews and guide FDE teams on scalable AI deployments. Candidates should have deep LLM knowledge, cloud experience (Azure/AWS/GCP), and strong Python/Java skills, with 10–20 years of high-end

Qualifications

• Deep understanding of LLM architecture, RAG, tool use, agent orchestration.
• Experience with model gateways, routing, and deployment options.
• Strong distributed systems and API design knowledge.
• Cloud platforms (Azure, AWS, GCP) and Kubernetes.
• Enterprise security, privacy, and governance expertise.

Responsibilities

• Define enterprise-grade architecture patterns for AI applications and agentic systems.
• Design common model access, routing, and provider-neutral integration patterns.
• Define reusable agent runtime, orchestration, tool, and API patterns.
• Establish common approaches for context, knowledge, retrieval, memory, and data access.
• Design evaluation, tracing, observability, and cost instrumentation across AI workloads.
• Work closely with FDE teams on active customer and internal deployments.
• Identify repeated implementation patterns that should be built once and reused.
• Provide architecture guidance without slowing down early experimentation.
• Create reference architectures, services and accelerators that make the next deployment faster.
• Ensure field learning feeds back into the common platform and product architecture.
• Define patterns for APIs, events, identity, authorization, data access and enterprise application integration.
• Guide cloud, private and hybrid deployment architecture for AI workloads.
• Establish scalable approaches for model serving, gateways, secrets, networking, and runtime isolation.
• Guide build-versus-buy decisions for models, platforms, frameworks, and infrastructure.
• Define technical controls for agent authority, tool permissions, human approvals, and auditability.
• Establish architecture for security, privacy, model governance, and responsible AI requirements.
• Define production standards for reliability, fallback, observability, incident handling, and lifecycle management.
• Ensure models and frameworks remain replaceable, and products do not become tightly coupled to a single provider.
• Lead architecture reviews for significant AI initiatives and production deployments.
• Coach Lead and Senior FDEs on architecture and engineering trade-offs.
• Maintain a practical architecture roadmap based on product and field learning rather than technology fashion.
• Challenge unnecessary complexity and prevent both fragmented experimentation and architecture-led bureaucracy.

Skills

LLM architecture

Agent orchestration

Model lifecycle

Cloud architecture

Tools

Docker

Kubernetes

CI/CD

Python

Java

Job description

Technical Architect - AI & Innovation

Experience:10-20 Years

About Role

The Technical Architect - AI & Innovation builds the common technical foundation that allows Azentio Forward Deployed AI teams and product teams to move quickly without repeatedly rebuilding the same infrastructure.

The role owns architecture across model access, agent orchestration, tools, APIs, context and knowledge, evaluation, observability, security, and deployment. It stays close to real customer implementations so that architecture is shaped by what teams actually need in production.

The objective is to balance speed and control: enable rapid experimentation while ensuring successful proofs can become secure, reusable, and enterprise-grade products.

Responsibilities

Common AI Architecture

• Define enterprise-grade architecture patterns for AI applications and agentic systems.
• Design common model access, routing, and provider-neutral integration patterns.
• Define reusable agent runtime, orchestration, tool, and API patterns.
• Establish common approaches for context, knowledge, retrieval, memory, and data access.
• Design evaluation, tracing, observability, and cost instrumentation across AI workloads.

Forward Deployed Engineering Enablement

• Work closely with FDE teams on active customer and internal deployments.
• Identify repeated implementation patterns that should be built once and reused.
• Provide architecture guidance without slowing down early experimentation.
• Create reference architectures, services and accelerators that make the next deployment faster.
• Ensure field learning feeds back into the common platform and product architecture.

Enterprise Integration & Platform

• Define patterns for APIs, events, identity, authorization, data access and enterprise application integration.
• Guide cloud, private and hybrid deployment architecture for AI workloads.
• Establish scalable approaches for model serving, gateways, secrets, networking, and runtime isolation.
• Guide build-versus-buy decisions for models, platforms, frameworks, and infrastructure.

Governance, Security & Reliability

• Define technical controls for agent authority, tool permissions, human approvals, and auditability.
• Establish architecture for security, privacy, model governance, and responsible AI requirements.
• Define production standards for reliability, fallback, observability, incident handling, and lifecycle management.
• Ensure models and frameworks remain replaceable, and products do not become tightly coupled to a single provider.

Technical Leadership

• Lead architecture reviews for significant AI initiatives and production deployments.
• Coach Lead and Senior FDEs on architecture and engineering trade-offs.
• Maintain a practical architecture roadmap based on product and field learning rather than technology fashion.
• Challenge unnecessary complexity and prevent both fragmented experimentation and architecture-led bureaucracy.

Required Technical Skills

AI & Agentic Systems

• Deep understanding of LLM architecture, RAG, tool use, agent orchestration, evaluation, and model lifecycle.
• Strong understanding of model gateways, routing, hosted and open-model deployment options, and inference trade-offs.
• Ability to assess agent frameworks and patterns without becoming dependent on a specific framework.

Software & Distributed Systems

• Strong architecture depth across distributed systems, microservices, APIs and event-driven systems.
• Strong software engineering understanding across Python, Java, and enterprise application stacks.
• Ability to review code-level and system-level designs and diagnose cross-layer architectural issues.

Data & Integration

• Strong knowledge of relational databases, enterprise data architecture, and data integration patterns.
• Strong understanding of APIs, messaging, identity, authorization, and tool interfaces used by agents.

Cloud & Platform Engineering

• Deep experience with Azure, AWS or GCP and cloud-native architecture.
• Strong knowledge of Docker, Kubernetes, CI/CD, observability, networking, security, and production operations.

Governance & Production AI

• Experience designing enterprise controls for security, auditability, privacy, evaluation, and responsible AI.
• Ability to design architectures that support human approval, bounded agent authority, and full action traceability.

Preferred

• AI platform architecture in enterprise products.
• BFSI or ERP product architecture experience.
• Large-scale production AI deployment experience.
• Experience building reusable platform capabilities from repeated customer or product implementations.