Senior AI Platform Engineer

Jobtailor; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Jobtailor is seeking a GenAI backend engineer in Bengaluru to design and build enterprise-grade GenAI applications, leveraging LangChain, LangGraph, and modern ML tooling.

You will own backend services with Python, FastAPI, and scalable architectures, integrated with vector stores, RAG pipelines, and secure production operations. You will contribute to governance and observability across projects.

Qualifications

• Bachelor's degree in computer science, information technology, engineering, or related discipline.
• 4+ years of professional software engineering with GenAI focus.
• Strong Python for AI app development and backend engineering.
• Experience building LLM-powered apps, RAG systems, agentive workflows, enterprise AI integrations.
• Hands-on with LangChain and LangGraph.
• Exposure to AutoGen, Google Agent SDK, MCP, or skills-based agent frameworks.
• Experience vector search with Azure AI, Pinecone, FAISS, Redis Vector.
• Strong understanding of embeddings, semantic search, chunking, retrieval optimization.
• Experience FastAPI, REST APIs, microservices, event-driven architectures.
• Hands-on with Azure OpenAI and Azure AI Services.
• Knowledge of Docker, Kubernetes, OpenShift.
• Experience CI/CD with GitHub Actions, GitLab CI.
• Strong SQL/NoSQL skills (MongoDB, Redis, ClickHouse).
• Understanding AI governance, privacy, compliance, responsible AI.
• Analytical, problem-solving, communication, collaboration.
• Ability to translate business requirements into scalable solutions.
• Willingness to learn and innovate.

Responsibilities

• Design and develop enterprise-grade GenAI applications with LangChain, LangGraph, AutoGen.
• Build and deploy agentic AI architectures including multi-agent workflows.
• Develop and maintain RAG pipelines for ingestion, chunking, embeddings, indexing, retrieval.
• Implement semantic search and knowledge retrieval with vector databases.
• Design scalable, reliable, secure AI system architectures.
• Contribute to AI evaluation, observability, monitoring, performance optimization.
• Design backend services with Python, FastAPI, REST APIs, microservices, event-driven architectures.
• Develop reusable platform components, services, APIs, integrations.
• Integrate AI solutions with enterprise systems and data services.
• Troubleshoot and optimize pipelines, vector stores, and cloud-native apps.
• Deploy and manage applications using Docker, Kubernetes, OpenShift, cloud services.
• Build CI/CD pipelines with GitHub Actions, GitLab CI; ensure production readiness.
• Ensure governance, security, privacy, compliance, and responsible AI.
• Collaborate with data engineers, cloud/platform, security, product, and stakeholders.
• Participate in architecture, code, testing, and design reviews.
• Support production operations with troubleshooting and performance tuning.

Skills

GenAI App Dev

Python

LangChain LangGraph

CI/CD

AI Governance

Enterprise Integrations

Monitoring Observability

Data Privacy

Collaboration

Continuous Learning

FastAPI

Vector Search

REST APIs

Education

Bachelor's degree in CS/IT/Eng

Tools

Docker

Kubernetes

OpenShift

Azure OpenAI

Pinecone

FAISS

Redis

Job description

• Design and develop enterprise-grade GenAI applications using LangChain, LangGraph, AutoGen, Google Agent SDK, and Model Context Protocol
• Build and deploy agentic AI architectures, including multi-agent workflows, tool/function calling, enterprise integrations, and autonomous decision-making systems
• Develop and maintain RAG pipelines covering document ingestion, chunking, embeddings, vector indexing, retrieval optimization, and response grounding
• Implement semantic search and knowledge retrieval using vector databases
• Design scalable, reliable, secure, and high-performing AI system architectures
• Contribute to AI evaluation, observability, monitoring, and performance optimization
• Design and build scalable backend services with Python, FastAPI, REST APIs, microservices, and event-driven architectures
• Develop reusable AI platform components, services, APIs, and integrations
• Integrate AI solutions with enterprise systems, third-party applications, workflow platforms, and data services
• Troubleshoot and optimize AI pipelines, APIs, vector stores, backend services, and cloud-native applications
• Deploy and manage applications using Docker, Kubernetes, OpenShift, and cloud-native services
• Build and maintain CI/CD pipelines using GitHub Actions, GitLab CI, and modern DevOps tooling
• Ensure production readiness through monitoring, observability, automated testing, release management, and operational excellence
• Support deployment and lifecycle management across development, testing, staging, and production environments
• Implement controls for PII protection, data privacy, AI security, compliance, and responsible AI
• Support AI governance through monitoring, auditability, access controls, and compliance frameworks
• Collaborate with data engineers, cloud and platform teams, security teams, product owners, and business stakeholders
• Participate in architecture, code, testing, and technical design reviews
• Support production operations through troubleshooting, performance tuning, root-cause analysis, and continuous improvement

Requirements

• Bachelor’s degree in computer science, Information Technology, Engineering, or a related discipline
• 4+ years of professional software engineering experience with strong exposure to GenAI, LLMs, platform engineering, and backend development
• Strong hands‑on expertise in Python for AI application development and backend engineering
• Experience building LLM‑powered applications, RAG systems, agentic workflows, prompt engineering solutions, and enterprise AI integrations
• Hands‑on proficiency with LangChain and LangGraph
• Exposure to AutoGen, Google Agent SDK, Model Context Protocol (MCP), or skills‑based agent frameworks
• Experience implementing vector search using Azure AI Search, Pinecone, FAISS, Redis Vector, or pgvector
• Strong understanding of embeddings, semantic search, chunking strategies, retrieval optimization, ranking, and context management
• Experience developing scalable backend services using FastAPI, REST APIs, microservices, and event‑driven architectures
• Hands‑on experience with Azure OpenAI and Azure AI Services
• Knowledge of Docker, with exposure to Kubernetes and OpenShift
• Experience implementing CI/CD pipelines using GitHub Actions, GitLab CI, or similar DevOps platforms
• Strong understanding of SQL and NoSQL databases including MongoDB, Redis, ClickHouse, and scalable data architectures
• Experience supporting enterprise AI systems through monitoring, observability, evaluation, and production operations
• Understanding of AI governance, security, privacy, compliance, and responsible AI frameworks
• Strong analytical and problem‑solving capabilities
• Excellent communication and stakeholder management skills
• Ability to translate complex business requirements into scalable technical solutions
• Strong collaboration skills across engineering, product, and business functions
• Commitment to engineering excellence, continuous learning, and innovation

Core Competencies

Demonstrates expertise in designing and developing enterprise-grade GenAI applications, with strong capabilities in Python, LangChain, and backend engineering. Proficient in implementing scalable AI architectures, CI/CD pipelines, and ensuring compliance with AI governance and security standards.

Highest-signal resume keywords

• GenAI Application Development
• Python Programming
• LangChain and LangGraph Proficiency
• CI/CD Pipeline Implementation
• AI Governance and Compliance

Hard Skills

• Python
• GenAI
• LLMs
• RAG Systems
• FastAPI
• REST APIs
• Vector Search
• SQL Databases
• NoSQL Databases
• Embeddings

Soft Skills

• Analytical Problem‑Solving
• Communication
• Stakeholder Management
• Collaboration
• Continuous Learning

Industry Keywords

• AI Architecture
• Enterprise Integrations
• Monitoring and Observability
• Data Privacy
• Responsible AI

Tools & Technologies

• Docker
• Kubernetes
• OpenShift
• GitHub Actions
• GitLab CI
• Azure OpenAI
• Azure AI Services
• Pinecone
• FAISS
• Redis Vector