AI/ML Engineer.

Latent Bridge Pvt Ltd; Maharashtra; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

LatentBridge Pvt Ltd in Pune, Maharashtra, is seeking a senior AI/ML engineer to lead end-to-end delivery of Agentic AI platforms on Azure, including RAG, fine-tuning, and production deployment. You will own architecture, governance, and client-facing engagement while guiding a cross-functional delivery team.

The role requires 12+ years in software AI with hands-on production experience and expertise in LLMs, retrieval systems, and MLOps.

Qualifications

• 12+ years in software engineering/AI with hands-on production experience.
• Deep experience with LLMs and RAG architectures: retrieval design, vector DBs, embeddings.
• Expertise in Agentic frameworks and orchestration (LangChain, LangGraph).
• Production MLOps/ModelOps: CI/CD for models, model registry, automated testing, monitoring, drift detection.
• Data engineering basics: SQL/NoSQL, ETL, data lineage and data privacy controls.
• Security & compliance: secrets management, access controls, vulnerability remediation, data encryption in transit & at rest.

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.

Skills

Production AI/ML

LLMs & RAG

Agentic frameworks

MLOps

Data engineering basics

Security & compliance

Tools

LangChain

LangGraph

Pinecone

Weaviate

Milvus

Prometheus

Grafana

ELK

Terraform

ARM

Job description

Pune Division, Maharashtra, India, India | Posted on 08/03/2026

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 Summary

We are seeking an experienced, hands‑on Sr AI/ML Engineer (8-12 years) to lead technical delivery and client engagement for Agentic AIplatforms. The role owns end‑to‑end design, productionisation andoperationalisation of LLM‑based solutions (RAG, fine‑tuning, model serving) onAzure, and will act as the technical face to the client while leading a cross‑functionaldelivery team.

What Success Looks like

• Deliver productionAgentic AI features that meet SLA targets for latency, throughput andreliability.
• Reduce time‑to‑valuefor LLM integrations via repeatable patterns, MLOps pipelines and reusablecomponents.
• Maintain modelgovernance, explainability and security posture appropriate for sensitive datadomains.

Key Responsibilities

• Architect and buildAgentic AI systems: orchestrate agents, action executors, retrieval layers, andfeedback loops.
• Design and implementLLM 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 forretrieval: vector stores, semantic search, indexing, embeddings, data privacy& access controls.
• Implement modelexplainability, confidence scoring, adversarial protections and prompt security(prompt injection mitigation).
• Define and enforcemodel governance: versioning, reproducibility, lineage, audit trails andcompliance.
• Collaborate withproduct, data engineering, security, DevOps and UX to ensure integrateddelivery and acceptance.
• Mentor and upskillengineers; conduct technical reviews and pair programming; recruit whenrequired.
• Act as primarytechnical contact for clients: present designs, lead architecture reviews, andsupport RFP/interview processes.

Required Skills & Experience

• 12+ years in softwareengineering/AI with demonstrable, hands‑on production experience.
• Deep experience withLLMs and RAG architectures: retrieval design, vector DBs(Pinecone/Weaviate/Milvus), embeddings.
• Expertise in Agenticframeworks and orchestration (LangChain, LangGraph, custom agent frameworks).
• ProductionMLOps/ModelOps: CI/CD for models, model registry, automated testing, monitoring(Prometheus/Grafana/ELK), drift detection.
• Data engineeringbasics: 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 withhybrid/multi‑cloud deployments and avoiding provider lock‑in.
• Familiarity withLangGraph, agentic safety patterns, and adversarial robustness testing.
• Prior exposure tofinancial data or private markets / regulated data handling.
• Experience with modelexplainability tools (SHAP, LIME, integrated gradients) and bias/fairnesstesting.
• Familiarity withTerraform/ARM for infra as code and GitOps workflows.