AI Architect

Latent Bridge; Pune District, Bengaluru, Bhopal; On-site; INR 4,500,000 - 7,500,000; Full time

Job summary

LatentBridge is seeking an experienced AI Architect (12+ years) to lead technical delivery for Agentic AI platforms. You will own end-to-end design, productionisation, and operationalisation of LLM-based solutions on Azure, while guiding a cross-functional delivery team and serving as the client-facing technical lead.

Responsibilities include building Agentic AI systems, implementing RAG and prompt engineering, and ensuring governance, security, and compliance.

Qualifications

• 12+ years in software engineering/AI with production experience.
• Deep experience with LLMs and RAG architectures.
• Proven track record in fine-tuning LLMs and prompt engineering.
• Expertise in Agentic frameworks and orchestration.
• Production MLOps/ModelOps: CI/CD for models, monitoring, drift detection.
• Data engineering basics: SQL/NoSQL, ETL, data privacy controls.
• Security & compliance: secrets management and data encryption.

Responsibilities

• Architect and build Agentic AI systems: orchestrate agents, action executors, retrieval layers, and feedback loops.
• Design and implement LLM solutions (RAG, retrieval chains, prompt engineering, fine-tuning) for production use.
• Own model deployment, serving and scaling on Azure (Azure AI, Azure ML, AKS).
• Build MLOps pipelines: CI/CD for models, automated testing, monitoring, drift detection and rollbacks.
• Lead data pipelines for retrieval: vector stores, semantic search, embeddings, data privacy controls.
• Implement model explainability and prompt security (prompt injection mitigation).
• Define and enforce model governance: versioning, reproducibility, lineage, audit trails and compliance.
• Collaborate with product, data engineering, security, DevOps and UX for integrated delivery.
• Mentor engineers; conduct technical reviews; recruit when required.
• Act as primary technical contact for clients; present designs and lead architecture reviews.

Skills

LLMs & RAG architectures

Agentic frameworks

MLOps / ModelOps

Data engineering basics

Security & compliance in data

Tools

Pinecone

Weaviate

Milvus

LangChain

LangGraph

Azure ML

Prometheus/Grafana/ELK

Job description

India-Pune, Bangalore, Bhopal, India | Posted on 08/14/2026

At LatentBridge, we make digital transformation work—without the noise.

We help enterprises simplify the complex, using AI orchestration and intelligent automation to solve real operational challenges. Our focus is on the parts of the business that are often overlooked—compliance, audit, policy, and risk—where precision, scalability, and accountability matter most.

Our products—Epic AI and IntellixCore—are built for this depth.Epic AI delivers enterprise-grade search and knowledge assistance directly inside the tools people already use—making access to information instant and intuitive.

IntellixCore is a platform to build, deploy, and scale intelligent agents across critical workflows—seamlessly integrating with existing systems.

We work with global banks, financial institutions, and legal firms—teams navigating constant change, regulatory pressure, and operational complexity.

In 2024, we were named one of the Deloitte UK Technology Fast 50, recognised for our growth and the tangible impact we deliver.

What sets us apart is not just what we build, but how we build it—grounded in enterprise realities, delivered with clarity, and always focused on outcomes that matter.

Job Description

Job Title: AI Architect

Experience Required: 12+ years

Engagement: [Full-time]

Project Type: High-visibility R&D initiative in financial research & data intelligence

Job Summary:

We are seeking an experienced, hands‑on AI Architect (12+ years) to lead technical delivery and client engagement for Agentic AI platforms. The role owns end‑to‑end design, productionisation and operationalisation of LLM‑based solutions (RAG, fine‑tuning, model serving) on Azure, and will act as the technical face to the client while leading a cross‑functional delivery team.

What Success Looks like:

• Deliver production Agentic AI features that meet SLA targets for latency, throughput and reliability.
• Reduce time‑to‑value for LLM integrations via repeatable patterns, MLOps pipelines and reusable components.
• Maintain model governance, explainability and security posture appropriate for sensitive data domains.

Key Responsibilities:

• Architect and build Agentic AI systems: orchestrate agents, action executors, retrieval layers, and feedback loops.
• Design and implement LLM solutions (RAG, retrieval chains, prompt engineering, fine‑tuning/LoRA/P-tuning) for production use.
• Own model deployment, serving and scaling on Azure (Azure AI, Azure ML, AKS, container registries) and hybrid setups.
• Build MLOps & ModelOps pipelines: CI/CD for models and services, automated testing, monitoring, drift detection and rollbacks.
• Lead data pipelines for retrieval: vector stores, semantic search, indexing, embeddings, data privacy & access controls.
• Implement model explainability, confidence scoring, adversarial protections and prompt security (prompt injection mitigation).
• Define and enforce model governance: versioning, reproducibility, lineage, audit trails and compliance.
• Collaborate with product, data engineering, security, DevOps and UX to ensure integrated delivery and acceptance.
• Mentor and upskill engineers; conduct technical reviews and pair programming; recruit when required.
• Act as primary technical contact for clients: present designs, lead architecture reviews, and support RFP/interview processes.

Required Skills & Experience:

• 12+ years in software engineering/AI with demonstrable, hands‑on production experience.
• Deep experience with LLMs and RAG architectures: retrieval design, vector DBs (Pinecone/Weaviate/Milvus), embeddings.
• Proven track record in fine‑tuning/customising LLMs (LoRA, full‑fine tune, instruction tuning) and prompt engineering.
• Expertise in Agentic frameworks and orchestration (LangChain, LangGraph, custom agent frameworks).
• Production MLOps/ModelOps: CI/CD for models, model registry, automated testing, monitoring (Prometheus/Grafana/ELK), drift detection.
• Data engineering basics: SQL/NoSQL, ETL, schema design, data lineage and data privacy controls.
• Security & compliance: secrets management, access controls, vulnerability remediation, data encryption in transit & at rest.

Good-to-Have Skills:

• Experience with hybrid/multi‑cloud deployments and avoiding provider lock‑in.
• Familiarity with LangGraph, agentic safety patterns, and adversarial robustness testing.
• Prior exposure to financial data or private markets / regulated data handling.
• Experience with model explainability tools (SHAP, LIME, integrated gradients) and bias/fairness testing.
• Familiarity with Terraform/ARM for infra as code and GitOps workflows.