Associate Software Engineer (AI/ML)

HARMAN

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4 days ago

Key Responsibilities

Designing and executing automated Agentic AI operational sequences leveraging open-source tool architectures, custom planners, and volatile memory elements.
Building and scaling secure backend software, RESTful configurations, and data endpoints via Python-centric microframeworks (such as Django, Flask, FastAPI, Robyn, or Vert.x ecosystem tracks).
Deploying highly accurate Retrieval-Augmented Generation (RAG) components utilizing embedded vector spaces and custom databases.
Writing structured background automation schedules and asynchronous job workers utilizing Celery.
Managing operational records, storage layouts, and search indices across relational and non-relational configurations including MySQL, Redis, ElasticSearch, and ScyllaDB.
Containerizing application components inside secure Docker instances to streamline orchestration inside agile environments across the Software Development Life Cycle (SDLC).

Skills & Eligibility

Degree Requirement: Must hold or be a final-year graduating student completing a Bachelor’s Degree in Computer Science, AI/ML, Data Science, or a closely matching quantitative engineering track.
Core Foundations: A rock-solid structural understanding of core programming paradigms, data structures, and foundational software lifecycle basics.
Experience: 0 to 1 year of hands-on technical software execution or clear, demonstrable academic project portfolios in predictive modeling.
Practical familiarity with **Agentic AI** mechanics (autonomous agent tool integration, reasoning protocols, memory buffers, and programmatic planning).
Academic or personal project integrations using Hugging Face pipelines, LangChain orchestration layers, or LlamaIndex data frameworks.
Familiarity with Retrieval-Augmented Generation (RAG) concepts and vector database clustering models.
Exposure to containerized orchestration models like Docker, Kubernetes, alongside CI/CD pipelines (Jenkins, Git, Bitbucket) and tracking stacks (Kibana).
Basic operational awareness of cloud ecosystem architectures like Amazon Web Services (AWS) for infrastructure or model staging.
Practical familiarity with **Agentic AI** mechanics (autonomous agent tool integration, reasoning protocols, memory buffers, and programmatic planning).
Academic or personal project integrations using Hugging Face pipelines, LangChain orchestration layers, or LlamaIndex data frameworks.
Familiarity with Retrieval-Augmented Generation (RAG) concepts and vector database clustering models.
Exposure to containerized orchestration models like Docker, Kubernetes, alongside CI/CD pipelines (Jenkins, Git, Bitbucket) and tracking stacks (Kibana).
Basic operational awareness of cloud ecosystem architectures like Amazon Web Services (AWS) for infrastructure or model staging.
Candidates holding a full-time B.E., B.Tech, B.Sc., or BCA degree specializing in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related computational branches are eligible to apply.

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