Software Engineer I, AI Solutions & Platforms

What matters most

• Deliver production-ready AI features

  The role designs, develops and deploys Generative AI services, including RAG and agentic workflows, for internal and customer-facing systems. Prepare to explain how an AI feature moves from a working prototype to tested, maintainable production code.

• Build reusable Python backend services

  The posting emphasizes Python microservices, REST APIs and hands-on contributions to service architecture, data models and integrations with existing backend and scientific applications. Focus preparation on concrete API and integration design decisions.

• Make AI services reliable and secure

  The role calls for attention to performance, reliability, authentication, rate limiting and observability, as well as troubleshooting and optimization in production. Be ready to explain how these concerns shape implementation and testing.

• Connect data processing to retrieval and LLMs

  The work includes Pandas and NumPy pipelines, vector search with PostgreSQL/pgvector and Qdrant, and LLM integration through Azure OpenAI, Anthropic Claude or compatible APIs. Prepare to describe how data reaches a RAG service and how its components interact.

• Deliver with cross-functional teams

  The engineer helps define requirements with product, architecture, engineering, R&D, data science, IT, Q&A and regulatory colleagues, then delivers against agile/scrum commitments. Documentation, code reviews and clear technical communication are also stated responsibilities.

Likely challenges to think through

• Balancing AI capability with service safeguards

  A likely challenge is adding RAG or agentic behavior without sacrificing API reliability, security or performance; the posting does not identify a current problem. Prepare a design walkthrough that makes authentication, rate limiting, observability and failure handling explicit.

• Keeping retrieval useful as data moves through pipelines

  A plausible trade-off is between pipeline and vector-search efficiency and the quality of information retrieved for an LLM. Prepare to discuss how you would validate data processing and examine retrieval results before optimizing the service.

• Turning varied requirements into a scoped delivery

  Input from scientific, engineering and regulatory stakeholders could create competing objectives, though no conflict is stated. Prepare an example of clarifying requirements, documenting a technical choice and breaking work into testable agile increments.

Topics to prepare

• RAG and vector search design

  Posting: RAG solutions and vector search using PostgreSQL/pgvector and Qdrant are part of the role. Suggested exercise: diagram how documents become searchable records and how retrieved results support an answer; explain where retrieval quality can fail.

• LLM integration choices

  Posting: LLM integration uses Azure OpenAI, Anthropic Claude, or OpenAI-compatible APIs. Suggested exercise: compare how a service would handle authentication, errors, and response differences when calling two of these interfaces.

• Agentic workflow design

  Posting: production-grade agentic workflows are a responsibility. Suggested exercise: sketch a bounded workflow with decision points, failure handling, and a clear stopping condition; explain how it integrates with an existing application.

• Production REST service architecture

  Posting: Python microservices and secure, scalable RESTful APIs must address performance, reliability, authentication, rate limiting, and observability. Suggested exercise: review a service design against each concern and identify one trade-off.

• ETL and data models

  Posting: the role includes data models, integrations, and Pandas/NumPy data pipelines. Suggested exercise: map source fields to a clean data model and describe how to detect invalid, missing, or duplicate records before indexing.

Skills to revise

• Build a Python REST endpoint

  Posting: strong Python backend and REST API experience is required; FastAPI is preferred, not required. Suggested exercise: implement a small endpoint with input validation, authentication handling, and tests for success and error responses.

• Implement a small RAG path

  Posting: building or integrating RAG and implementing vector search with PostgreSQL/pgvector or Qdrant are relevant duties. Suggested exercise: index a small sample set in one named store, retrieve matches for a question, and inspect whether the returned records support the answer.

• Process data with Pandas and NumPy

  Posting: familiarity with ETL and processing workflows using Pandas and NumPy is requested. Suggested exercise: clean a small table, flag missing values and duplicates, and produce a reproducible output suitable for downstream retrieval.

• Test and deliver a change

  Posting: Git-based workflows, CI/CD, automated testing, code reviews, and production troubleshooting are specified. Suggested exercise: prepare a small code change with unit tests, a review-ready explanation, and a CI/CD check plan; describe how you would diagnose a failing test.

• Translate requirements across teams

  Posting: the engineer collaborates with product, architecture, R&D, data science, IT, Q&A, and regulatory teams and delivers against agile/scrum commitments. Suggested exercise: turn a brief AI-service request into clarifying questions, acceptance criteria, and a concise technical explanation for a non-engineering stakeholder.

• Map the stated requirements to evidence

  Prepare a one-page list of your education, dated software-engineering and AI work, and examples involving Python, REST APIs, LLM integration and RAG. Mark any gaps rather than implying experience you do not have.

• Rehearse a RAG project walkthrough

  Choose a real project, if available, and prepare a short account of its retrieval flow, LLM integration, your contribution, testing and trade-offs. If using a practice example, label it as hypothetical.

• Sketch a production AI service

  As a practice exercise, draw a Python microservice and REST API that connects an LLM to an existing backend or scientific application. Be ready to explain authentication, rate limiting, observability, reliability and performance choices; the posting names these concerns but does not prescribe a design.

• Work through data and vector-search choices

  Prepare a small example showing how you would process data with Pandas or NumPy and make it searchable with PostgreSQL/pgvector or Qdrant. Note what you would measure or test; do not present the exercise as a company project.

• Gather delivery and collaboration examples

  Select concrete examples of automated tests, Git and CI/CD workflows, troubleshooting, documentation or code review. Prepare one account of translating requirements with cross-functional partners and delivering an agile/scrum commitment.

• Write questions for the employer

  Ask how this team evaluates RAG and agentic solutions, integrates with scientific applications, and works with R&D, Q&A and regulatory colleagues. If relevant, confirm expectations for the fully onsite Bengaluru schedule.