Role Overview

The Senior Associate Engineer (Full Stack + GenAI) is a mid-entry-level position on an Agile software development team building and supporting Bain's most strategic internal platforms. This role is distinguished by its focus on modern full-stack engineering — spanning React frontends, Python backends, and SQL/NoSQL data layers — combined with hands-on development of Generative AI-powered features and workflows. Team members collaborate closely to design, build, and iterate on enterprise-scale applications that serve Bain's global user base.

Key Responsibilities

Technical Delivery (80%)

• Build and maintain enterprise full-stack applications using React (frontend) and Python (backend) with RESTful or GraphQL APIs.
• Design and interact with relational (PostgreSQL, SQL Server) and non-relational (MongoDB, DynamoDB, Redis) databases.
• Develop and integrate GenAI features including: LLM-powered assistants, RAG (Retrieval-Augmented Generation) pipelines, prompt engineering, and AI-driven workflow automation.
• Participate fully in Agile/Scrum ceremonies — sprint planning, standups, retrospectives, and demos.
• Translate user stories into technical subtasks; deliver committed work within sprint timelines.
• Follow established application architecture and design patterns; work with seniors to ensure scalability, performance, and security standards are met.
• Write unit and integration tests (pytest, Jest/React Testing Library) as part of each story's definition of done.
• Contribute to CI/CD pipelines using GitHub Actions or equivalent DevOps tooling.
• Support deployed production applications through bug diagnosis, performance monitoring, and data investigation.
• Actively develop T-shaped skills: expand into areas like MLOps, data pipelines, UX design, or infrastructure as team needs arise.
• Maintain technical documentation — API specs, architecture decision records (ADRs), and runbooks — with guidance from seniors.

Research & Innovation (10%)

• Evaluate emerging tools in GenAI, LLM frameworks (LangChain, LlamaIndex, etc.), vector databases (Pinecone, Weaviate, pgvector), and ML serving infrastructure.
• Contribute to proofs-of-concept and technical spikes; present findings and recommendations to the broader Software Development team.
• Assess and adopt supplemental technologies (e.g., new embedding models, fine-tuning approaches, agentic frameworks) as project needs evolve.
• Participate in internal knowledge-sharing sessions on AI/ML concepts and best practices.

Communication (10%)

• Articulate technical findings, design trade-offs, and GenAI experiment results clearly to both technical and non-technical stakeholders.
• Proactively surface blockers and dependencies, ensuring the team maintains a shared understanding of story completion criteria.
• Provide constructive input during sprint retrospectives to improve team velocity and experience.

Qualifications

• Bachelor's degree or equivalent in Computer Science, Engineering, or a related field.
• 1–3 years of professional experience in software development; full-stack preferred.
• Demonstrated experience delivering features in Agile/Scrum environments.
• Strong analytical and problem-solving skills; ability to break down ambiguous requirements.
• Clear communication skills; comfortable presenting to peers and stakeholders.

Technical Skills

• Frontend -
  React, JavaScript (ES6+), TypeScript, HTML5, CSS3, Tailwind CSS / Bootstrap, Redux / Zustand
• Backend -
  Python, FastAPI / Django / Flask, REST APIs, GraphQL, Celery (async tasks)
• SQL / RDBMS -
  PostgreSQL, Microsoft SQL Server, T-SQL, query optimization, schema design
• NoSQL –
  MongoDB, DynamoDB, Redis (caching), Cosmos DB, Elasticsearch
• GenAI (Required) -
  LLM integration (OpenAI, Azure OpenAI, Anthropic), Prompt Engineering, RAG pipelines, LangChain / LlamaIndex, vector stores (Pinecone, pgvector, Weaviate)
• Cloud -
  Microsoft Azure (primary), AWS basics; blob storage, serverless functions, managed AI services
• DevOps / CI-CD -
  GitHub Actions, Docker, basic Kubernetes awareness, environment config management
• Testing -
  pytest, unittest; Jest, React Testing Library; integration and load testing awareness
• AI/ML (Good to Have) -
  scikit-learn, model evaluation, fine-tuning (LoRA / PEFT), MLflow / MLOps basics, Hugging Face Transformers