Role Overview:

We are looking for a hands-on Product Architect / Engineer to design and build an AI-driven operations platform focused on intelligent automation, insights, and optimization across cloud, network, and application operations.This role blends product thinking, architecture, and deep engineering across AI/ML and Agentic AI systems.

Key Responsibilities:

• Architect and build the agentic AI platform leveraging AI, ML, and Agentic AI frameworks
• Design multi-agent systems for use cases for operations domains like Cloud Ops, NetworkOps, ServiceDesk, root cause analysis, anomaly detection, and predictive insights
• Develop data pipelines and telemetry fabric for ingesting and processing large-scale operational data (logs, metrics, traces, topology)
• Build and integrate LLM-powered copilots and autonomous agents for operations 
• Define product architecture, including microservices, APIs, event-driven systems, and scalable cloud-native patterns
• Translate business needs into AI-driven product features and accelerators
• Collaborate with product, operations, and engineering teams to deliver end-to-end capabilities

Key Skills:

• Strong experience in product architecture and full-stack engineering
• Hands-on expertise in:
• AI/ML (model development, inference, MLOps)
• LLMs and Agentic AI frameworks (multi-agent orchestration, reasoning systems)
• Data engineering (streaming, batch, real-time analytics)
• Proficiency in Python and modern frameworks
• Experience with cloud platforms (AWS, Azure) and cloud-native architecture
• Agent framework experience – Microsoft agent framework, langgraph and langchain.
• Strong understanding of:
• Observability systems (logs, metrics, traces)
• AIOps / IT operations platforms
• API design and microservices architecture
• Familiarity with vector databases, embeddings, RAG architectures, and knowledge graphs

Good to Have:

• Experience building autonomous AI agents for IT/network/application operations
• Exposure to FinOps, Cloud Cost Optimization,
• Knowledge of network topology, infrastructure ops, or ITSM tools
• Experience with real-time decisioning systems and event correlation engines

The Role

We are looking for an elite engineer who builds like a hacker and communicates like a consultant. You will not just be building models in a notebook; you will be deployed to the front lines to build, architect, and ship end-to-end Agentic AI products directly for our most strategic customers.

This is a high-stakes role. You will face "tricky" customers who have vague requirements and high expectations. Your job is to translate their chaos into technical order, write production-grade code, and deploy autonomous agents that solve their actual business problems.

What You Will Do

• Build & Deploy (60%):

Design and ship full-stack AI applications. You own the pipeline from data ingestion to the LLM inference layer to the frontend interface.

Architect autonomous agentic workflows (using frameworks like LangChain, LangGraph, or custom implementations) that can reason, plan, and execute complex tasks.

Write robust, clean, and highly testable code (Python/Typescript). "It works on my machine" is not acceptable; it must work in the customer's restricted cloud environment.

Handle DevOps and MLOps constraints: Dockerizing agents, managing Kubernetes clusters, and optimizing inference latency.

• Customer Engineering (40%):

Serve as the technical face of the company for our most demanding accounts.

Navigate complex stakeholder environments. You will push back on unrealistic demands, clarify ambiguous requirements, and steer "tricky" customers toward technically feasible solutions without breaking rapport.

Translate technical constraints into business value for non-technical executives.

Generate high-quality data for our customers and ability to review the data generated by other experts. This is one of the most critical aspects of the role.

The "Must-Haves"

• Experience: 8–12 years of total engineering experience. You started as a strong Software Engineer and evolved into an AI specialist.
• Education: B.S./M.S. in Computer Science, Math, or Physics from a top-tier institution (IIT, MIT, Stanford, Harvard, Berkeley, CMU, or similar global top-ranking universities).
• Deep AI Fluency: You aren't just calling APIs. You understand RAG implementation nuances, vector database optimization, fine-tuning (LoRA/PEFT), and the architecture of agentic loops.
• Production Engineering: Experience with end-to-end deployment (AWS/GCP/Azure, CI/CD pipelines, Terraform, Docker).

The "Why You" (Soft Skills)

• Thick Skin & High EQ: You don't get flustered when a customer changes their mind for the third time in a week. You can hold your ground with a CTO and explain "why not" to a CEO.
• Extreme Attention to Detail: You catch the edge cases that others miss. You obsess over error handling, retry logic in agents, and data privacy.
• Grit: You enjoy the "messy" work of integrating legacy enterprise data just as much as prompting the latest model.

What will set you apart

• Public Code: A visible GitHub footprint is very helpful. We want to see your side projects, your contributions to open source, or your experiments with LLMs. Please include your GitHub link in your application.

Tech Stack

• Languages: Python (Expert).
• AI/Data (Expertise in all is not required): PyTorch, LangChain, LlamaIndex, Pinecone/Weaviate, OpenAI/Anthropic APIs, HuggingFace.
• Infra (Expertise in all is not required): Kubernetes, Terraform, AWS/GCP.