Staff AI Engineer

The Role

• We're looking for a Staff Software Engineer to own critical systems and shape the technical direction of our intelligence ecosystem.
•  You'll go deep on one or two systems -- designing their internals, scaling their infrastructure, hardening their reliability -- while driving architectural decisions that ripple across the full platform.
• This is a hands-on role with broad influence. You'll ship code daily -- often in partnership with AI coding tools like Claude Code -- driving them to produce robust, test-driven code at a pace that multiplies your impact.
• You'll set technical standards through reviews and design docs, and collaborate closely with engineers across the team.
• We expect you to mentor, uplift, and make the people around you better.
• You'll operate at the intersection of three pillars: deep business understanding and customer obsession, leveraging generative AI to solve real problems, and designing architecture that is robust and scalable.
• Your decisions here directly shape products used by global brands. This is a hybrid role based in Pune. Competitive compensation and benefits 

Systems You'll Shape

• You won't be limited to a single domain. Our ecosystem spans multiple interconnected systems -- here is where a Staff engineer makes an impact:
•  Agentic Data Collection & Competitive Intelligence -- Know everything about a brand's digital presence before a human asks. Build intelligent agents that discover, collect, and keep product and competitor data fresh across marketplaces at scale.
• Product Intelligence & Attribute Normalization -- Make every product across Pattern's ecosystem describable, searchable, and comparable. Build the semantic engine that turns messy, unstructured product data into a consistent taxonomy using LLMs, vector search, and hierarchical classification.
• Marketplace Behavioral Analytics -- Answer the question: what makes a customer buy one product over another? Combine consumer behavior data, competitive analysis, and attention modeling to surface the attributes that actually drive conversion.
• Creative Automation & Generative Pipelines -- Turn structured product intelligence into marketplace-ready content at scale. Build the systems that connect data to AI-generated imagery, video, and copy -- from model training to quality assurance to final output.
• Cross-System Architecture -- Make the ecosystem greater than the sum of its parts. Design the shared contracts, communication patterns, and feedback loops that keep these systems connected, observable, and self-improving. 

Our Stack

We hire engineering judgment, not keyword matches. If you've built serious systems on a different stack, we want to talk.
 
• Languages & Frameworks: Python, FastAPI, SQLAlchemy, React/Next.js, TypeScript
• Cloud & Infrastructure: AWS (we use a lot of it -- ECS, Lambda, S3, SQS/SNS, Bedrock, Step Functions, and more), Terraform
•  Data: PostgreSQL, Redis, Snowflake, DynamoDB, vector databases
•  ●AI/ML: LLMs, diffusion models, computer vision, NLP, speech-to-text -- deployed across AI infrastructure providers (RunPod, Replicate, Fal, and others) ● Orchestration: Temporal, Apache Airflow (Astronomer), LangGraph, Celery 

What You'll Do

• Own one or two systems end-to-end: understand the users and stakeholders who depend on them, then drive the architecture, implementation, operational health, and evolution.
•  Drive cross-system technical decisions -- data contracts, API boundaries, shared infrastructure, and integration patterns across the ecosystem.
• Design and build production AI systems: retrieval pipelines, agentic workflows, generative pipelines, and classification engines with the right cost/latency/quality trade-offs. 
• Set engineering standards through code reviews, design documents, and architectural RFCs. Your work becomes the reference implementation. 
• Partner with product, design, and stakeholders to frame problems worth solving -- then solve them end-to-end. 
• Identify and eliminate systemic bottlenecks -- whether in architecture, tooling, process, or team velocity. 
• Elevate the engineers around you through pairing, review rigor, and raising the bar on what "production-ready" means. 

You Should Have

• Deep experience building, operating, and evolving production software systems (typically 8+ years)
• Track record of owning systems -- not just features -- and making architectural decisions that held up over time. 
• Strong AI/ML engineering skills: you have built production systems that use LLMs, embeddings, classification pipelines, or generative models -- not just prototypes. 
• Excellent systems design instincts: distributed systems, data modeling, API design, event-driven architecture, and the judgment to know when simplicity beats sophistication. 
• Backend depth (Python, SQL, cloud infrastructure) with enough full-stack range to move confidently anywhere in the codebase. 

Even Better If You Bring

• Product sense and user empathy -- you care about whether the system actually solves the problem, not just whether it runs.
•  Communication that scales: you write clear design docs, give precise code reviews, and can explain complex trade-offs to both engineers and non-engineers.
• Comfort with ambiguity and R&D pace. Some of what we build has no precedent -- you find that energizing, not unsettling.