Staff Engineer Software - AI

Position Title: Staff Software Engineer
Skills Required:

Python, AI/ML, Vertex AI, Azure ML, LangChain, RAG, Vector Databases

Experience Range:

9 - 12 years

Roles & responsibilities:

• Hands-on development of AI-powered applications using Python, conducting code reviews and continuously improving application quality, scalability, and reliability.
• Performs advanced development, support, and implementation of enterprise AI solutions using specialized business and domain expertise.
• Partners with Architects to translate enterprise AI and GenAI strategies into detailed solution designs and implementation plans.
• Leads large AI and machine learning initiatives with limited or no oversight.
• Evaluates AI, Machine Learning, and Generative AI industry trends and recommends technologies and approaches that improve business outcomes.
• Proposes and champions innovative AI solutions, architectures, and engineering practices that enhance operational efficiency and customer experience.
• Sets standards to deliver high-quality AI products and services while maintaining responsible AI principles, governance, and security.
• AI Engineering: Designs and develops enterprise AI applications using Python, leveraging Large Language Models (LLMs), prompt engineering, and AI orchestration frameworks.
• AI Engineering: Designs and implements Retrieval Augmented Generation (RAG) architectures, embedding pipelines, semantic search solutions, and enterprise knowledge retrieval platforms.
• AI Engineering: Builds production-grade AI agents using LangChain, LangGraph, ADK, or similar frameworks including tool calling, memory management, orchestration, and human-in-the-loop workflows.
• Data & AI: Designs and implements vector search solutions using Azure AI Search, Vertex AI Vector Search, Pinecone, Weaviate, pgVector, or similar vector databases.
• Cloud AI: Deploys and manages machine learning and GenAI workloads using Vertex AI or Azure Machine Learning platforms, including training, inference, monitoring, and model lifecycle management.
• AI Integration: Designs and implements AI-driven integrations with enterprise platforms using REST APIs, Kafka, Apache Camel, event-driven architectures, and microservices.
• Computer Vision & Multimodal AI: Develops solutions involving image, video, OCR, document intelligence, and multimodal AI capabilities for business use cases.
• AI Governance: Implements responsible AI controls, model evaluation, tracing, observability, prompt security, guardrails, and performance monitoring.
• Quality Engineering: Defines testing and validation strategies for AI applications, including model evaluation, prompt testing, integration testing, and automated testing frameworks.
• Performance Engineering: Monitors and optimizes AI applications for response quality, latency, throughput, scalability, model cost, and resource utilization.
• Production Operations: Ensures AI services run reliably in production environments using an SRE mindset including observability, monitoring, tracing, incident management, and operational readiness.
• Mentors engineers on AI, ML, GenAI, Cloud AI platforms, architecture patterns, and engineering best practices.

Experience Required:

• 9+ years of software engineering experience developing enterprise applications.
• 4+ years of experience leading engineering teams and delivering large-scale technology initiatives.
• 5+ years of programming experience using Python and developing enterprise-grade applications.
• 3+ years of experience designing and implementing AI/ML or Generative AI solutions.
• Hands-on experience with Large Language Models (LLMs), prompt engineering, and AI-powered application development.
• Experience designing and implementing Retrieval Augmented Generation (RAG) solutions.
• Experience working with vector databases and semantic search technologies.
• Experience deploying AI/ML models using Vertex AI or Azure Machine Learning platforms.
• Experience developing cloud-native applications on Azure or GCP.
• Experience developing REST APIs, microservices, and event-driven architectures.
• Experience integrating enterprise systems using Kafka, Apache Camel, APIs, and messaging technologies.
• Experience implementing MLOps, CI/CD pipelines, model deployment, monitoring, and governance processes.
• Broad expertise with software development lifecycle methodologies including Agile practices.
• Experience analyzing and optimizing AI application performance, scalability, and operational cost.
• Experience implementing enterprise-grade security, compliance, and responsible AI practices.

Competencies:

• Compassionate and kind, showing courtesy, dignity, and respect. They show sincere interest and empathy for all others
• Foster innovation through creativity to get a workable solution. Use analytical thinking through issues using logic and reason
• Show integrity in what is done and how it is done - without sacrificing personal/business ethics
• Embrace an inclusion-focused mindset, seeking input from others on their work and encouraging the open expression of diverse ideas and opinions
• Team-oriented, positively contributing to team morale and willing to help
• Learning-Focused, finding ways to improvise in their field and use positive constructive feedback to grow personally and professionally
• Think strategically and proactively anticipate future problems, needs or changes in the work
• Delighting our customers and maintaining customer relationships are top priority and they work to always deliver solutions through this lens
• Retail Industry and eCommerce Experience is a must
• Extensive experience in mentoring development teams and delivering large scale application
• Strong expertise in ensuring the application is designed and can run in production environments with an SRE (Site Reliability Engineering) mindset
• Proficient in design patterns and architectural skills, with a proven track record of delivering large-scale initiatives while managing multiple parallel projects effectively

Mandatory Skills Required:

Programming Languages:

• Python
• SQL

AI & GenAI:

• Large Language Models (LLMs)
• Prompt Engineering
• LangChain
• LangGraph
• RAG Architecture
• Agentic AI Frameworks

Cloud AI Platforms:

• Google Vertex AI and/or Azure Machine Learning

Vector Databases:

• Azure AI Search
• Vertex AI Vector Search

AI Development:

• Embedding Models
• Semantic Search
• AI Agent Development
• Function Calling
• Knowledge Graphs
• MCP (Model Context Protocol)
• Azure AI Services
• Vertex AI Agent Builder
• Google Agent Development Kit (ADK)

Cloud Technologies:

• Azure or GCP
• Docker
• Kubernetes

Integration:

• REST APIs
• Apache Kafka
• Apache Camel
• Event-Driven Architecture
• FAST API

DevOps & MLOps:

• Azure DevOps or GitHub Actions
• CI/CD
• AI Model Deployment
• AI Monitoring

Databases:

• PostgreSQL
• MongoDB
• Cassandra
• Vector Databases

Domain:

• Supply Chain

Good to have Skills:

• Computer Vision & Vision AI
• OCR & Document Intelligence
• MLOps
• AI Evaluation Frameworks
• AI Security & Governance
• Responsible AI
• Prompt Security
• Feature Stores
• BigQuery or Azure Synapse
• Terraform
• Helm
• GitOps
• Real-time Event Streaming
• Distributed Systems Architecture