AI/ML Engineer.

Latentbridge; Pune District; On-site; INR 4,500,000 - 7,500,000; Full time

Job summary

Latentbridge in Pune, India seeks an experienced, hands-on Sr AI/ML Engineer (8-12 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 on Azure, and will act as the technical face to the client while leading a cross-functional delivery team.

You will architect and build Agentic AI systems, implement LLM solutions (RAG, retrieval chains, prompt engineering, fine‑tuning)

Qualifications

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

Responsibilities

• Architect and build Agentic AI systems: orchestrate agents, action executors, retrieval layers, 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, 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.

Skills

LLMs & RAG

LangChain

LangGraph

MLOps

Vector DBs

Embeddings

Security

Compliance

CI/CD

Monitoring

Data Eng

Architecture

Tools

Pinecone

Weaviate

Milvus

Azure ML

AKS

Job description

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 and reliability.
• 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.