Senior Software Engineer

Pylon Management Consulting; Gurugram District; Hybrid; INR 320,000 - 560,000; Full time

Job summary

Pylon Management Consulting is seeking a Senior AI Engineer to lead production-grade AI systems on Google Cloud. You will design, build, and operate GenAI and agentic AI services with cross-functional teams to deliver measurable value for brands and shoppers.

Role emphasizes hands-on implementation, architecture, and robust observability, security, and MLOps practices, working with the AI Lead Engineer, Data Science, Product, and Core Engineering groups.

Qualifications

• Production AI experience with real-world LLM/AI/ML deployments.
• GCP and Vertex AI proficiency for deployment, pipelines, and endpoints.
• Solid understanding of LLM basics, transformers, embeddings, and RAG.
• Experience building AI agents and multi-agent systems.
• Strong focus on AI security, prompt design, and data privacy.
• Proficiency in Python and API/microservices design.
• Experience with CI/CD, IaC, monitoring, and testing.

Responsibilities

• Design and implement GenAI/agentic solutions on GCP.
• Build autonomous AI agents and multi-step workflows.
• Define architecture, interfaces, and non-functional requirements.
• Implement guardrails for safety, data privacy, and prompt design.
• Integrate AI components into CI/CD pipelines and IaC.
• Instrument prompts, model executions, and agent calls for observability.
• Collaborate with AI Lead Engineer and cross-functional teams.

Skills

GCP Vertex AI

Agentic frameworks

Python

TypeScript/Java

MLOps

CI/CD

Observability

RAG/Vector Search

Security & safety

Job description

As a Senior AI Engineer, you will be a hands-on technical contributor designing, building, and operating production-grade Generative AI and agentic AI systems. Working alongside our AI Lead Engineer, Data Science, Product, and Core Engineering teams, you will transform complex retail media challenges into secure, scalable, and observable AI services that drive measurable value for brands and shoppers.

Key Responsibilities

• GCP & Vertex AI Architecture: Design and implement GenAI and agentic solutions on Google Cloud Platform, leveraging Vertex AI (models, endpoints, pipelines, Vector Search/RAG, and Agent Builder) along with core GCP cloud services.
• Agentic Workflows & MCP: Build autonomous AI agents and multi-step workflows featuring tool calling, orchestration, and integrations. Contribute to Model Context Protocol (MCP) servers and agent-to-agent/agent-to-application protocols to safely automate retail media use cases.
• Solution & Interface Design: Collaborate with the AI Engineering Manager and solution architects to define technical architecture, clear interface contracts, and non-functional requirements (security, reliability, performance, cost).
• Security & Governance: Implement robust guardrails for AI systems, including safe prompt and tool design, prompt-injection mitigation, data privacy controls, secrets management, and access controls.
• MLOps & DevOps: Integrate AI components into CI/CD pipelines for models, prompts, and agentic workflows using Infrastructure as Code (IaC), automated testing, and proper environment isolation.
• Observability & Evaluation: Instrument prompts, model execution, tool calls, and agents with tracing, logging, and metrics. Define evaluation frameworks including automated tests, offline/online evaluation, red-teaming, regression suites, and human-in-the-loop (HITL) workflows.
• Cross-Functional Execution: Write high-quality, production-ready code in Python (and TypeScript/Java where applicable). Participate in code reviews, design reviews, and incident post-mortems to continuously elevate AI engineering standards.

What Youll Need to Succeed

• Production AI Experience: Strong background in software, data, or ML engineering with proven, hands-on delivery of production-grade LLM or AI/ML solutions (moving beyond proof-of-concepts).
• GCP & Vertex AI Proficiency: Practical expertise on GCP and Vertex AI, including model deployment, pipeline construction, and RAG/Vector Search implementations.
• Core LLM & RAG Knowledge: Solid understanding of foundation model mechanics-transformers, tokenization, embeddings, context window management, RAG, and trade-offs between prompt engineering, adapters, and fine-tuning.
• Agentic Frameworks: Hands-on experience building AI agents, tool-augmented systems, or multi-agent orchestrations using modern AI frameworks.
• AI Security & Safety: Deep understanding of guardrails, reducing hallucinations, preventing prompt injection, and safely handling sensitive data.
• Engineering & MLOps: Proficiency in Python, API design, microservices, and event-driven architectures, along with MLOps best practices (CI/CD, IaC, monitoring, and automated testing).
• Collaborative Ownership: Strong analytical problem-solving skills, proactive communication, and the ability to collaborate smoothly with product managers, data scientists, and cross-functional business stakeholders.