Junior AI Engineer

Infosys
Posted on
Infosys logo
Experience
2 - 5 yrs
Job Location
Bengaluru, India
Vacancy
1
Designation
Junior Machine Learning Engineer
Job Type
ONSITE

Job Description

Educational Requirements
Bachelor of Engineering
Service Line
Data Analytics Unit
Responsibilities
  • GenAI / LLM Engineering: Build LLM-powered applications (chatbots, copilots, summarization, knowledge assistants) using OpenAI/Azure OpenAI/Anthropic/Gemini or open-source LLMs.
  • Implement RAG pipelines: data ingestion, chunking, embeddings, vector search, prompt assembly, response generation.
  • Improve response quality using prompt engineering, retrieval tuning (hybrid search, metadata filters), and basic RAG evaluation practices.
  • ML Engineering (non-platform): Develop and deploy ML components (classification, NLP, forecasting) using scikit-learn / PyTorch / TensorFlow as needed.
  • Package AI/LLM solutions into production-grade services using FastAPI/Flask.
  • Write clean, reusable Python modules and follow engineering best practices (testing, logging, code quality).
  • Deployment Operations (LLMOps exposure): Support deployment to cloud environments: AWS (SageMaker/ECS/Lambda) or Azure (Azure ML/AKS/App Services).
  • Implement basic observability: logs, error handling, latency tracking, token usage tracking (where applicable).
  • Assist in quality, safety, and governance practices: PII redaction, content filtering, prompt-injection mitigation, secure access controls.
Additional Responsibilities:
  • Vector databases: Pinecone / Qdrant / Chroma / Weaviate / FAISS.
  • Frameworks: LangChain / LangGraph / LlamaIndex / Semantic Kernel.
  • Evaluation tools: RAGAS / TruLens / DeepEval, prompt testing frameworks.
  • Containerization: Docker (Kubernetes is optional).
  • CI/CD exposure: GitHub Actions / Azure DevOps / Jenkins.
  • Data pipelines: Airflow / Prefect / Databricks.
  • Safety tooling: Presidio, content safety filters, access control patterns.
Technical and Professional Requirements:
  • Python programming (strong fundamentals, OOP, writing APIs, debugging).
  • Hands-on experience building GenAI/LLM solutions: RAG / embeddings / vector DB / prompt engineering.
  • Experience with FastAPI or Flask (building and serving APIs).
  • Understanding of LLM application lifecycle (prompting, evaluation, versioning, deployment basics).
  • Knowledge of at least one cloud platform: AWS or Azure.
  • Basic understanding of Git, code reviews, and deployment workflows.
Preferred Skills:
  • Technology->Artificial Intelligence->Artificial Intelligence - ALL
  • Technology->AI-Generative AI->Generative AI - Basic

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